Table of Contents
Overview
Definition of Causal Inference
Causal inference is a fundamental concept in statistics and data science that seeks to determine the cause-and-effect relationships between variables. Unlike correlation, which merely indicates that two variables move together, causal inference aims to establish whether changes in one variable directly result in changes in another.[1.1] The process of causal inference involves identifying and quantifying the causal effect of one variable on another, utilizing various statistical methods, study designs, and theoretical frameworks to establish causality while accounting for confounding factors, potential biases, and the limitations of observational data.[2.1] Specifically, causal inference refers to the design and analysis of data for uncovering causal relationships between treatment or intervention variables and outcome variables.[3.1] The design and analysis of data for uncovering causal relationships are central to causal inference, particularly in contexts such as treatment or intervention studies.[3.1] Notably, the work of statisticians like Paul Holland has emphasized that statistical inference is more suited for assessing the "effects of causes" rather than the "causes of effects," highlighting the nuanced nature of causal analysis.[4.1] Furthermore, causal inference plays a significant role in practical applications, such as designing interventions, where understanding the reasons behind specific behaviors can greatly influence the success of those interventions.[5.1] Causal inference can also be viewed as a specialized form of prediction, where the focus is on predicting outcomes under various manipulations.[6.1] This perspective is particularly relevant in data science, as it allows researchers to move beyond simple data descriptions to a deeper understanding of the underlying causal mechanisms.[7.1]Importance in Various Disciplines
Causal inference plays a crucial role across various disciplines, particularly in understanding how contextual factors influence outcomes. The context-mechanism-outcome (CMO) configuration is essential for explaining the causal relationships between contextual factors, the mechanisms they trigger, and the resulting outcomes. This framework highlights that the effectiveness of interventions can vary significantly based on the specific context in which they are applied, emphasizing the importance of considering these factors in social research.[24.1] In the realm of economics, for instance, the economy serves as a multifaceted contextual factor that shapes individuals' living conditions, access to resources, and societal inequality. Economic structures influence employment opportunities and working conditions, thereby affecting overall prosperity and social outcomes.[25.1] This illustrates how economic context can alter the interpretation of causal relationships, as interventions that may work in a prosperous economy might not yield the same results in a struggling one. Realist evaluation further underscores the significance of context in causal inference by identifying how interventions operate under varying circumstances. It posits that causal mechanisms are embedded within specific social processes, meaning that the same intervention may produce different outcomes depending on the context.[26.1] This variability is critical for understanding the efficacy of interventions across different settings, particularly in healthcare, where interventions that demonstrate efficacy in one context may fail in another.[27.1] Moreover, the implications of causal inference extend to the fields of medical and health policy research, where the appropriateness of causal language in observational studies is often debated. The limitations of current approaches to causal inference highlight the need for alternative frameworks that can better accommodate the complexities of real-world data and the influence of contextual factors.[40.1] Thus, recognizing the importance of context in causal inference is vital for accurate interpretation and application of research findings across disciplines.History
Early Theories and Methodologies
The study of causality has a long and rich history, deeply embedded in philosophical debates that have shaped its foundational principles. Central to this discourse is Aristotle, who proposed a comprehensive theory of causality, asserting that everything that exists or changes does so due to four distinct types of causes. This framework is integral to his scientific and philosophical investigations, where each Aristotelian science involves the causal exploration of specific domains of reality, ultimately leading to causal knowledge.[58.1] Over the centuries, the philosophical landscape surrounding causality has expanded, incorporating diverse ideas and arguments, including Hume's skepticism, which adds complexity to the understanding of cause and effect.[55.1] Despite this extensive historical background, the formal statistical concepts and methodologies that define the field of causal inference are relatively recent developments, emerging primarily in the twentieth century. This evolution reflects a significant shift from philosophical inquiry to a more structured statistical approach, highlighting the ongoing relevance of causality in both scientific and philosophical contexts.[48.1] The evolution of causal inference has been marked by the integration of traditional statistical methods, such as estimating equations and semiparametric theory, which have been instrumental in establishing causal relationships.[45.1] These methods have been further enhanced by contemporary research that explores the intersection of causal inference with machine learning, providing new identification strategies and improving the estimation of causal effects.[46.1] Causality has long been a subject of philosophical debate and remains a central scientific issue with a rich history. The statistical study of cause and effect, grounded in the notion of "fairness" in comparisons, dates back several hundred years; however, the statistical concepts and developments that constitute the area of causal inference are relatively recent, emerging only in the last few decades.[54.1] In contemporary research, particularly in public health, counterfactual frameworks and statistical methods are recognized as powerful tools that clarify scientific questions and guide analyses.[71.1] These methods are essential for investigating the effects of complex interventions or policies on various healthcare outcomes, as they support decision-making processes in health services research.[70.1]Key Contributors and Their Impact
Key philosophical figures have significantly shaped our understanding of causality, particularly through the works of David Hume and Immanuel Kant. Hume, a prominent British Empiricist, rigorously applied empirical standards to the concept of causation, arguing that all knowledge arises from experience and that the human mind lacks an innate ability to perceive causal relationships.[74.1] He introduced a skeptical, reductionist viewpoint on causality, suggesting that causation is understood through regularities observed in nature, encapsulated in his definition that causation is a universal generalization of the form "whenever C, then E".[74.1] Hume's investigations emphasized the empirical discovery of causes, positing that systematic investigation is essential for grounding scientific explanation and inference.[77.1] Kant's engagement with Hume's skeptical view of causality is a pivotal aspect of his philosophical work, particularly articulated in "Prolegomena to Any Future Metaphysics" (1783).[44.1] He posits that causality serves as a central example of a category or pure concept of understanding, underscoring the importance of his relationship with Hume in the broader context of his philosophy.[44.1] Kant's contributions to the discourse on causality can be examined through two intersecting metaphysical axes, as elaborated by Watkins in "Kant and the Metaphysics of Causality".[45.1] This detailed analysis of Kant's early causal realism reveals the depth of his philosophical inquiries and their implications for contemporary methodologies in causal inference.[45.1] The evolution of causal inference methodologies has been significantly influenced by advancements in statistical methods, machine learning, and the availability of large-scale data sets. These developments have enabled researchers to unravel complex causal relationships from observational data, providing a viable alternative to randomized controlled trials in fields such as healthcare and social sciences.[50.1] Notably, the integration of machine learning as an estimation strategy has enhanced the ability to address potential biases in estimating causal effects and to uncover heterogeneous causal effects.[52.1] Furthermore, contemporary methodologies now include approaches for high-dimensional data and precision medicine, as well as causal discovery techniques.[51.1] Researchers are increasingly focused on defining the population of interest, specifying target causal parameters, and assessing identifying assumptions, often utilizing directed acyclic graphs (DAGs) to guide their study designs.[51.1] These advancements collectively contribute to a more robust framework for causal inference, allowing for improved decision-making in various domains.[50.1]Recent Advancements
Integration with Machine Learning
The integration of causal inference with machine learning is becoming increasingly significant, particularly in the realm of healthcare. A new automated causal inference method, known as AutoCI, has been developed based on the invariant causal prediction (ICP) framework, which facilitates the causal reinterpretation of clinical trial data.[103.1] This method has demonstrated its effectiveness by enabling researchers to clearly identify causal variables in two real-world randomized controlled trials (RCTs) involving patients with endometrial cancer, utilizing extensive clinicopathological and molecular data.[116.1] Despite the method's ability to distinguish between non-causal and proxy variables, some proxy variables, such as MMRd, present challenges due to their borderline hazard ratios, which complicate their interpretation in clinical studies.[116.1] Nonetheless, the biological relevance of MMRd in the context of cancer prognosis has been supported by numerous well-designed experimental and translational studies.[116.1] Moreover, the intersection of big data analytics and causal inference is crucial for the advancement of precision medicine. The integration of extensive data sources with robust data science methodologies enables the encoding of domain causal knowledge and counterfactual reasoning, which is essential for improving health outcomes.[102.1] As machine learning algorithms become more prevalent in handling large datasets, their combination with causal reasoning is expected to qualitatively transform medical practices, thereby enhancing patient care.[102.1] The integration of machine learning techniques with propensity score analysis is becoming increasingly significant in the field of causal inference. Propensity scores play a central role in observational studies aimed at estimating causal effects, and recent advancements in machine learning have demonstrated their effectiveness across various scenarios, highlighting the broad applicability of these methods.[104.1] The intersection of machine learning and causal inference is expanding rapidly, with evidence suggesting that both stacked and linear estimations of nuisances yield equivalent results in these contexts.[105.1] Furthermore, in quasi-experimental designs, where non-randomized treatments can introduce substantial bias, the application of propensity score methods can effectively reduce this bias, thereby enhancing the evaluation of health policy impacts.[106.1] Real-world applications of automated causal inference techniques have demonstrated significant improvements in decision-making processes. For instance, the Causal Roadmap framework has been shown to enhance transparency and reproducibility in causal analyses, guiding analytic decisions beyond mere inference on causal parameters.[114.1] However, challenges remain, such as addressing biases related to selective survival and losses to follow-up, which can affect the validity of causal conclusions.[115.1] Overall, the integration of causal inference with machine learning not only enhances analytical precision but also supports the development of more effective interventions in healthcare and beyond.Novel Approaches and Techniques
Recent advancements in causal inference have introduced novel approaches and techniques that enhance the understanding and application of causal relationships among variables. One significant development is the use of directed acyclic graphs (DAGs), which serve as a graphical tool to represent causal systems underlying research questions. DAGs help improve study validity by clearly illustrating assumptions and potential confounding factors, thereby facilitating a more structured approach to causal inference in various fields, including health and social research.[111.1] Additionally, propensity score analysis has emerged as a prominent method for estimating causal effects, particularly in secondary data analysis. This technique utilizes the probability that a participant received a treatment based on observed baseline characteristics, allowing researchers to control for confounding variables and better isolate causal relationships.[92.1] Another innovative approach is the application of structural equation models (SEMs), which extend beyond traditional regression models by accommodating both observed and unobserved variables. SEMs allow for the representation of complex causal relationships, including reciprocal influences among variables, which is particularly useful in economic research and other fields where such dynamics are prevalent.[124.1] Recent advancements in causal inference (CI) have established it as a crucial research topic, focusing on inferring intrinsic causal relations among variables of interest.[95.1] This area of research has seen the emergence of automated causal inference methods, which are particularly relevant for analyzing independent and identically distributed (i.i.d) data as well as time series data.[93.1] These methods encompass a range of concepts, including manipulations, causal models, sample predictive modeling, causal predictive modeling, and structural equation models.[93.1] A significant approach within this domain is the constraint-based method, which plays a vital role in the ongoing development of causal discovery techniques.[93.1]Methodological Framework
Statistical Methods for Causal Inference
Statistical methods for causal inference are essential for establishing valid causal relationships in epidemiological research. These methods rely on several key principles and assumptions to ensure the accuracy of causal claims. One fundamental aspect is the requirement for strong assumptions, which include causal-structural subject-matter knowledge and careful statistical analysis to address potential confounding and bias.[131.1] Confounding variables pose significant challenges in observational studies, as they can lead to biased estimates of causal effects if not adequately controlled.[134.1] Researchers must identify and measure these confounders to minimize their impact on causal inference. For instance, confounding is defined as a source of bias that arises from a lack of comparability between exposed and non-exposed groups.[134.1] To address this, various strategies can be employed, such as stratification, standardization, multivariable regression analysis, and propensity score methods.[138.1] The high-dimensional propensity score (HD-PS) method, in particular, has been developed to enhance the identification of potential confounders through a data-driven approach.[138.1] Causal inference in observational studies is fundamentally dependent on several key identifying assumptions, one of which is the positivity assumption. This assumption stipulates that the probability of treatment must be bounded away from 0 and 1, meaning that for every combination of covariates, it should be possible to observe both treated and control subjects, thereby ensuring overlap in covariate distributions.[139.1] In the context of causal inference, studies typically require multiple assumptions, such as Unconfoundedness, to make valid causal statements, with the positivity assumption being a critical component often referred to as 'Common Support' or 'Overlap'.[141.1] Violations of the positivity assumption can manifest in two forms and pose significant threats to the identifiability of causal effects, as a lack of variability in treatment assignment may hinder the ability to uniquely determine or estimate these effects based on observed variables.[142.1] Thus, ensuring that the positivity assumption holds is essential for maintaining the integrity of causal inferences drawn from observational data.In this section:
Applications
Causal Inference in Social Sciences
Causal inference plays a pivotal role in the social sciences, where understanding causal relationships is essential for addressing complex societal issues. Researchers in this field often employ various techniques to derive insights from observational data, particularly when randomized controlled trials are not feasible. Traditional interpretations of causal studies have been largely confined to randomized controlled trials; however, the increasing importance of observational data has emerged as a response to the demand for generalizable and timely evidence to inform decision-making by regulators, payers, and healthcare providers.[170.1] The application of causal inference methods encompasses a range of strategies designed to estimate treatment effects accurately. These methods include propensity score matching, covariate matching, and advanced causal machine learning techniques, which utilize algorithms such as meta-learners and tree-based models to estimate conditional average treatment effects.[167.1] Researchers must carefully define the population of interest and specify target causal parameters while assessing identifying assumptions through subject matter expertise, often aided by directed acyclic graphs (DAGs).[168.1] Causal inference in social sciences is significantly advanced through the application of Causal Machine Learning, which integrates causal inference with machine learning techniques to uncover true cause-and-effect relationships in complex, high-dimensional data, thereby surpassing traditional correlation-based predictions.[183.1] However, bias and confounding present substantial challenges in studies evaluating treatment effectiveness based on observational data, complicating the process of drawing valid causal inferences. To mitigate these issues, investigators must rigorously assess threats to the validity of their findings and estimate the strength and direction of suspected biases.[174.1] Furthermore, to facilitate drawing causal inferences from observational data, a conceptual framework centered around "four targets"—target estimand, target population, target trial, and target validity—can be employed.[176.1] In the context of policy evaluation, causal inference methodologies illuminate the pathways of impact and inform decision-making processes. For instance, studies examining the effects of educational policies, such as class size on student performance, utilize regression discontinuity designs to derive causal insights.[181.1] The overarching goal of these applications is to ensure that causal inference methods are robust enough to inform policy decisions, despite the unique challenges posed by the assumptions required for valid causal inference.[182.1]Causal Inference in Medicine
Causal inference plays a critical role in medicine, particularly in the context of observational studies where treatment effects must be assessed while accounting for confounding variables. The selection of variables in these studies is essential to ensure that the identified causal relationships are both robust and relevant to public health outcomes. However, achieving perfect adjustment for all measured baseline variables is often unattainable, leading to ambiguity regarding which variables should be prioritized in the analysis.[186.1] Recent advancements in variable selection methods, such as the outcome-adaptive lasso, have shown promise in addressing the challenges posed by a large number of spurious covariates—those that are unrelated to the outcome or exposure. This method has demonstrated the ability to select a propensity score model that includes all true confounders and predictors, thereby enhancing the reliability of causal inferences drawn from observational data.[187.1] Moreover, the integration of domain knowledge is pivotal in guiding the selection of covariates. This approach challenges the traditional reliance on purely statistical measures, emphasizing the importance of understanding the underlying processes and relationships within the data. Such knowledge not only informs variable selection but also has significant implications for experimental design, model-building, and the interpretation of results.[188.1] For instance, in Bayesian-based causal structure inference, incorporating domain knowledge can enhance model stability and interpretability, particularly in fields like industrial processes where causal features directly relate to quality variables.[189.1]In this section:
Challenges And Limitations
Issues with Assumptions and Validity
Causal inference is fraught with challenges related to the assumptions that underpin its validity. One significant issue is the reliance on the positivity assumption, which is essential for valid causal inference. This assumption necessitates that the measures used in a study directly relate to the scientific questions being investigated, ensuring that the data collected is appropriate for the research context at hand.[206.1] Furthermore, studies aiming to draw causal inferences must address potential violations of key assumptions such as conditional exchangeability, positivity, and consistency.[204.1] These assumptions are critical, as their violation can lead to inaccurate estimates of causal effects.[208.1] In the context of international large-scale assessments (ILSA), the methodological constraints further complicate the validity of causal claims. The data available for ILSA is often limited to what can be comparably collected across different countries, which can obscure the causal relationships that educational policy research seeks to explore.[211.1] Moreover, the validity of causal research using ILSA data hinges on the assumption that the causal questions and research designs are equally valid across all participating countries.[212.1] This assumption is particularly challenging to meet, given the diverse educational contexts and policies that exist globally. Additionally, the complexities of causality within ILSAs highlight the tenuous nature of the inferences drawn from such datasets. Researchers have noted that the increasing trend of using ILSA data for causal inferences often overlooks fundamental limitations and assumptions inherent in these datasets.[213.1] Despite previous caution against making causal claims with ILSA data, recent initiatives have shown a growing interest in this area, reflecting a shift in the research community's approach to utilizing these datasets for causal analysis.[214.1] To mitigate these challenges, researchers are encouraged to adopt rigorous methodologies that critically assess the validity of the assumptions underlying their causal inferences. This includes employing strategies such as sensitivity analyses to examine the potential impact of unmeasured confounding and measurement bias.[205.1] By addressing these issues, researchers can enhance the robustness of their causal claims and contribute more effectively to the field of educational policy-making.Misattribution and Methodological Concerns
Misleading causal inferences can arise from the limitations of International Large-Scale Assessments (ILSAs) due to various methodological concerns. One significant issue is unobserved heterogeneity, which refers to differences among observational units, such as countries, that correlate with both independent and dependent variables. If these differences are not adequately controlled for, they can lead to biased conclusions regarding causal relationships.[226.1] Additionally, the correlational nature of ILSA data, stemming from its cross-sectional study design, further complicates the ability to draw valid causal inferences. This limitation may exclude ILSA data from meta-analyses in educational research, as the data does not support causal claims effectively.[227.1] Moreover, the assumption of no unmeasured confounders is critical for the validity of comparative observational research. However, this assumption is often questionable in practice, as the presence of unmeasured confounders can violate the strong ignorability assumption, resulting in biased treatment effect estimations and undermining the credibility of the findings.[232.1] Despite the importance of addressing unmeasured confounding, the quantitative assessment of its potential impact is rarely reported in the literature.[230.1] In the evaluation of health policies, particularly in the context of vaccine safety and effectiveness, unmeasured confounding poses significant challenges. A systematic review revealed that only 42 out of 913 studies on vaccine safety and effectiveness employed methods to detect or correct for unmeasured confounding, indicating a critical gap in the research.[231.1] Furthermore, the quantitative assessment of the potential influence of unmeasured confounders in observational data analysis is rare, despite the common reliance on the "no unmeasured confounders" assumption.[229.1] This highlights the necessity for improved methodological strategies to address unmeasured confounding, which is essential for enhancing the validity of causal inferences drawn from observational studies. Measurement errors also pose a substantial challenge to causal inference. The presence of measurement error can induce systematic bias, complicating the estimation of causal effects. Various algebraic and graphical methods have been proposed to mitigate the impact of measurement errors, particularly in the context of partially observable confounders.[233.1] Furthermore, the methodological literature on measurement error is extensive, yet statisticians and econometricians are only beginning to incorporate these issues into formal causal inference frameworks.[236.1]In this section:
Future Directions
Emerging Trends in Causal Inference
Emerging trends in causal inference research are characterized by the integration of advanced methodologies and interdisciplinary approaches. One significant direction is the application of causal inference techniques to high-dimensional data, which presents unique challenges such as extracting complex causal relationships and managing computational complexity. Traditional methods in causal discovery are categorized into local and global approaches, with local methods being more computationally efficient but limited in their applicability to high-dimensional contexts.[249.1] The statistical and computational challenges posed by high-dimensional datasets include issues like spurious correlations and confounding bias, which complicate causal analysis.[250.1] Another notable trend is the incorporation of machine learning within the causal inference framework. This integration aims to enhance the estimation of treatment effects and evaluate heterogeneity in causal relationships. Machine learning methods can reduce bias from model misspecification, as they do not require a predefined functional form.[246.1] However, the use of complex algorithms can introduce new biases and under-coverage, necessitating careful consideration in their application.[247.1] Moreover, while machine learning offers flexibility in modeling, it can also lead to wider confidence intervals due to increased variance.[248.1] In the ecological domain, there is a growing recognition of the need to strengthen experimental design and clarify assumptions for deriving causal inferences from both experimental and observational data. This integration can provide new insights into ecological causal questions, although barriers such as the lack of comprehensive resources and the complexity of interdisciplinary jargon remain.[255.1] As the field evolves, it is essential to prioritize the development of best practices for causal inference in ecology, which can facilitate a more rigorous assessment of causal relationships.[242.1]References
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[4] Causal inference - Wikipedia — Sociologist Herbert Smith and Political Scientists James Mahoney and Gary Goertz have cited the observation of Paul Holland, a statistician and author of the 1986 article "Statistics and Causal Inference", that statistical inference is most appropriate for assessing the "effects of causes" rather than the "causes of effects". Qualitative methodologists have argued that formalized models of causation, including process tracing and fuzzy set theory, provide opportunities to infer causation through the identification of critical factors within case studies or through a process of comparison among several case studies. These methodologies are also valuable for subjects in which a limited number of potential observations or the presence of confounding variables would limit the applicability of statistical inference.[citation needed]
[5] What Is Causal Inference? - O'Reilly — Causal inference also enables us to design interventions: if you understand why a customer is making certain decisions, such as churning, their reason for doing so will seriously impact the success of your intervention. ... if the statistics of incoming links say it is, that's good enough. No semantic or causal analysis is required….
[6] Causal Inference - an overview | ScienceDirect Topics — Causal inference is merely special case of prediction in which one is concerned with predicting outcomes under alternative manipulations. The conditionality problem illustrates how the introduction of a causal component into a statistical model can resolve previous ambiguities in choice of a statistical procedure.
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[26] Contextual factors influencing variation in implementation and outcomes ... — Realist evaluation is a theory-driven evaluation that involves identifying causal explanations of how interventions work, for whom and under what circumstances. Causal mechanisms are always embedded within particular contexts and social processes, so that Rounds might work differently in different situations, and, as a result of different contexts, trigger mechanisms that generate outcomes
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[45] Title: Causal inference: critical developments, past and future - arXiv.org — In this paper, we review core tenets and methods of causal inference and key developments in the history of the field. We highlight connections with traditional `associational' statistical methods, including estimating equations and semiparametric theory, and point to current topics of active research in this crucial area of our field.
[46] PDF — directions. Some of the most exciting areas of development lie at the intersection of causal inference with machine learning (Athey & Imbens 2017, 2019; Huber 2021). This review describes several key identification strategies for causal inference and how machine learning methods can enhance our estimation of causal effects.
[48] Causal inference: critical developments, past and future — Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of `fairness' in comparisons dates back several hundred years, and yet statistical concepts and developments that form the area of causal inference are only decades old. In this paper, we review core tenets and methods
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[51] The Future of Causal Inference - PMC - PubMed Central (PMC) — These include methods for high-dimensional data and precision medicine, causal machine learning, causal discovery, and others. For example, researchers who are well versed in causal inference ideas will typically take great care in defining the population of interest, specifying the target causal parameter(s), assessing identifying assumptions using subject matter knowledge (possibly with the help of directed acyclic graphs (DAGs)), designing the study to emulate a target trial, choosing efficient and robust estimators, and carrying out sensitivity analysis. In order to specify, for example, a propensity score model or an outcome model (or both) to make causal inference, we need to learn about observed data distributions or functions (such as mean functions).
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[70] The role of causal inference in health services research I: tasks in ... — The role of causal inference in health services research I: tasks in health services research - PMC In a recent issue of the American Journal of Public Health, Hernán and other colleagues strongly plea for causal thinking in scientific research where the research question investigates consequences of decisions and interventions (Ahern 2018; Begg and March 2018; Chiolero 2018; Glymour and Hamad 2018; Hernán 2018a, b; Jones and Schooling 2018). Health services research (HSR) supports decision making by investigating the effect of complex ‘interventions’ or ‘policies’ on different healthcare system outcomes (Glass et al. Unfortunately, public health decisions on interventions or policies are often only based on ‘descriptive’ and ‘modeled’ results, without the integration of a solidly principled causal inference framework.
[71] Evaluating Public Health Interventions: 5. Causal Inference in Public ... — Counterfactual frameworks and statistical methods for supporting causal inference are powerful tools to clarify scientific questions and guide analyses in public health research. Counterfactual accounts of causation contrast what would happen to a
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[77] PDF — Key Points of Hume's Theory of Causation 209 1.1. Whether A Causes B is an Objective Matter of Fact, and Causes—whether Superficial or Hidden—Can Be Discovered by Systematic Investigation Hume's investigation of human nature is focused on the empirical discovery of causes, since only this can ground scientific explanation and inference to
[92] The causal inference framework: a primer on concepts and methods for ... — The purpose of this first paper is to: a) define causal inference, b) provide a brief history of the causal inference framework and associated methods, c) review an example of how such methods have strengthened research in a different area of science, and d) introduce the reader to 2 approaches for causal inference that are particularly relevant to the study of well-women and low-risk perinatal processes: directed acyclic graphs (DAGS), and propensity score analysis. While DAGs provide a graphical tool to represent the causal system underlying a research question so that study validity is enhanced, propensity score techniques are a set of analytical methods for estimating causal effects that make use of Rosenbaum’s propensity score.26 Like many analytical approaches for causal inference, propensity scores are most common in secondary data analysis but have application in primary data collection as well.56 A propensity score is the probability that a participant received a treatment based on observed, measurable baseline characteristics.
[93] Causal discovery and inference: concepts and recent methodological ... — This paper aims to give a broad coverage of central concepts and principles involved in automated causal inference and emerging approaches to causal discovery from i.i.d data and from time series. After reviewing concepts including manipulations, causal models, sample predictive modeling, causal predictive modeling, and structural equation models, we present the constraint-based approach to
[95] Causal Inference with Latent Variables: Recent Advances and Future ... — Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial research topic. ... Peter Spirtes and Kun Zhang. 2016. Causal discovery and inference: Concepts and recent methodological advances. In Appl. Inform. Google Scholar Peter L Spirtes, Christopher Meek, and Thomas S
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[103] Automated causal inference in application to randomized controlled ... — Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data.
[104] Improving propensity score weighting using machine learning — For both uses of the propensity scores the machine learning methods performed well in a variety of scenarios, indicating the broad applicability of these results. ... The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41-55. ... Robins JM. Doubly robust estimation in missing data and
[105] How to select predictive models for decision-making or causal inference ... — The intersection between machine learning and causal inference is growing rapidly ... In these settings, stacked and linear estimations of the nuisances perform equivalently. Detailed analysis (Appendix A.7, Supplementary Fig. S13 ... Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects.
[106] Machine Learning Algorithms to Estimate Propensity Scores in ... - MDPI — (1) Background: Quasi-experimental design has been widely used in causal inference for health policy impact evaluation. However, due to the non-randomized treatment used, there is great potential for bias in the assessment of the results, which can be reduced by using propensity score (PS) methods. In this context, this article aims to map the literature concerning the use of machine learning
[111] How to use directed acyclic graphs: guide for clinical researchers — Directed acyclic graphs are commonly used to illustrate and assess the hypothesised causal mechanisms in health and social research. These graphs can illuminate investigators' assumptions and help clearly describe each possible explanation for associations observed in data given researchers' assumptions, ranging from causal effects to confounding and selection bias, and thereby help
[114] An application of the Causal Roadmap in two safety monitoring case ... — The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. Conclusion:
[115] Observational Studies: Methods to Improve Causal Inferences — The selective survival and losses to follow-up bias occur when the association between a risk factor (predictor variable) and a health outcome (dependent variable) differs among the participants who drop out or stay in the study. For example, to control for obesity in a study of sedentary behavior as a predictor of MI, each case (a participant with an MI) is matched to one or more control participants with a similar BMI (i.e., a case with BMI 31, a control with BMI 31). To demonstrate this concept, let’s say we conducted a case-control study to assess the association between myocardial infarction (MI) (case) and sedentary behavior (outcome) among obese and non-obese study participants. Retrieved from https://journals.lww.com/ajnonline/Fulltext/2021/01000/Selection_of_the_Study_Participants.22.aspx [DOI] [PMC free article] [PubMed] [Google Scholar]
[116] Automated causal inference in application to randomized ... - Nature — Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. Despite the clear cut-off between non-causal and proxy variables provided by AutoCI, some of the proxy variables present borderline hazard ratios, for instance, MMRd. Due to small effect size, clinical studies45,46 usually associate MMRd with intermediate patient prognosis, not much different from the prognosis of NSMP EC; however, from the biological perspective, MMRd is highly relevant for a well-defined cascade of molecular changes in cancer cells with favourable prognostic impact as proven by a large number of well-designed experimental and translational studies31,47.
[124] PDF — 1 Introduction Structural equation models (SEMs), also called simultaneous equation models, are multivariate (i.e., multi-equation) regression models. Unlike the more traditional multivariate linear model, however, the response variable in one regression equation in an SEM may appear as a predictor in another equation; indeed, variables in an SEM may influence one-another reciprocally, either
[131] Principles of Causation - StatPearls - NCBI Bookshelf — Contemporary studies involving causality require strong assumptions, causal-structural subject-matter knowledge, careful statistical analysis, and considerations for alternative explanations. The following models demonstrate the core principles of causation. Proposed by Rothman, this model defines cause as an event, condition, or characteristic necessary for disease occurrence, emphasizing that a disease results from multiple components acting together. This model aids in understanding the multifactorial nature of disease causation in epidemiology.A cause (or set of causes) may contribute to a causal relationship if it is: Conclusion Establishing causation in epidemiology requires strong assumptions, causal-structural subject-matter knowledge, and careful study design and statistical analysis considerations. Addressing challenges, such as confounding and other forms of bias, is essential for ensuring the validity of effect estimates, ultimately guiding effective public health interventions and improving patient care.
[134] Methodological issues of confounding in analytical epidemiologic ... — Confounding is the main issue in observational etiologic studies and non-randomized interventional studies as well (3-5). In the context of epidemiology, confounding is a source of bias in estimating causal association and it corresponds to a lack of comparability between the exposed and non-exposed groups (or cases and controls) .
[138] Control of confounding in the analysis phase - an overview for ... — Using examples from large health care database studies, this article provides the clinicians with an overview of standard methods in the analysis phase, such as stratification, standardization, multivariable regression analysis and propensity score (PS) methods, together with the more advanced high-dimensional propensity score (HD-PS) method. In order to attempt to reduce this drawback, the HD-PS approach was developed.35 The HD-PS method involves a series of conceptual steps,35 which in essence can be condensed to: 1) specification of data source; 2) data-driven selection of potential confounders; 3) estimation of PS; 4) use of the PS to make groups of interest comparable and assessment of group comparability and 5) estimation of the association between treatment/exposure and outcome.
[139] [2110.10266] Addressing Positivity Violations in Causal Effect ... — In observational studies, causal inference relies on several key identifying assumptions. One identifiability condition is the positivity assumption, which requires the probability of treatment be bounded away from 0 and 1. That is, for every covariate combination, it should be possible to observe both treated and control subjects, i.e., the covariate distributions should overlap between
[141] causality - Positivity assumption in causal inference with continuous ... — In causal inference, studies usually require several assumptions (e.g., Unconfoundedness) to make valid causal statements. One of these assumptions is the 'Positivity' Assumption (sometimes referred to as 'Common Support' / 'Overlap').
[142] Core Concepts in Pharmacoepidemiology: Violations of the Positivity ... — In other words, this lack of variability in treatment assignment would threaten the identifiability of causal effects—whether they can be uniquely determined or estimated based on observed variables—in both this subgroup and the overall population that includes this subgroup. Positivity violations can take two forms.
[167] Causal Inference and Causal Machine Learning with Practical Applications: — The tutorial will cover techniques of observational causal inference like propensity and covariate matching, Causal ML techniques of conditional average treatment effect estimation, using wide variety of algorithms like meta-learners, direct uplift estimation, tree-based algorithms.
[168] The Future of Causal Inference - PMC — These include methods for high-dimensional data and precision medicine, causal machine learning, causal discovery, and others. For example, researchers who are well versed in causal inference ideas will typically take great care in defining the population of interest, specifying the target causal parameter(s), assessing identifying assumptions using subject matter knowledge (possibly with the help of directed acyclic graphs (DAGs)), designing the study to emulate a target trial, choosing efficient and robust estimators, and carrying out sensitivity analysis. In order to specify, for example, a propensity score model or an outcome model (or both) to make causal inference, we need to learn about observed data distributions or functions (such as mean functions).
[170] What is Causal Inference? - Carelon Research — Causal study interpretations have traditionally been restricted to randomized controlled trials; however, causal inference using observational data is growing in importance, driven by the need for generalizable and rapidly delivered real-world evidence to inform regulatory, payer, and patient or provider decision making.
[174] Methods for Enhancing Causal Inference in Observational Studies — Bias and confounding are major issues in studies that assess treatment effectiveness based on observational data, making causal inference difficult. Investigators must conduct a rigorous assessment of threats to the validity of their findings and estimate the strength and direction of suspected bias.
[176] Four targets: an enhanced framework for guiding causal inference from ... — To facilitate drawing causal inference from observational data, we introduce a conceptual framework centered around "four targets"—target estimand, target population, target trial, and target validity.
[181] Causal inference: Causal Inference in Policy Evaluation: Assessing ... — The following case studies illustrate the practical application of causal inference in policy evaluation, showcasing how this methodology can illuminate the pathways of impact and inform decision-making. 1. Education Policy: A study on the effect of class size on student performance utilized a regression discontinuity design. This method took
[182] Causal Inference Challenges and New Directions for Epidemiologic ... — Like all research aiming to draw causal inferences, studies on the health effects of social policies require strong assumptions and must address potential violations of conditional exchangeability, positivity, and consistency, among others (Box 1) [9 •, 13]. However, policy studies face unique challenges to these assumptions.
[183] Causal Inference with Machine Learning: Why It Matters in Business ... — Causal Machine Learning combines causal inference with machine learning to identify true cause-and-effect relationships in complex, high-dimensional data, going beyond traditional correlation -based predictions.
[186] PDF — Observational studies of treatment e ects require adjustment for confounding vari-ables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance
[187] Outcome-adaptive lasso: Variable selection for causal inference — This proposed approach can perform variable selection in the presence of a large number of spurious covariates, that is, covariates unrelated to outcome or exposure. We present theoretical and simulation results indicating that the outcome-adaptive lasso selects the propensity score model that includes all true confounders and predictors of
[188] PDF — Our exploration of causal inference principles underscores the pivotal role of domain knowledge in guiding co-variate selection, challenging the common reliance on statistical measures. This understanding carries implications for experimental design, model-building, and result interpretation.
[189] What is the role of domain knowledge in Causal inference? — Enhancing Model Stability and Interpretability Domain knowledge can be used as a prior in Bayesian-based causal structure inference, which enhances the stability and interpretability of models. For instance, in industrial processes, incorporating domain knowledge helps in learning causal features that are directly related to quality variables, thus improving the performance of soft sensors
[204] Causal Inference Challenges and New Directions for Epidemiologic ... — Like all research aiming to draw causal inferences, studies on the health effects of social policies require strong assumptions and must address potential violations of conditional exchangeability, positivity, and consistency, among others (Box 1) [9•, 13]. An accessible introduction to the two main approaches to causal inference used in non-randomized studies of the health effects of social policies (confounder-control and instrument-based methods), with a glossary cross walking commonly used terms in econometrics and epidemiology. Although instrument-based methods in health research are commonly used to evaluate the effect of a social resource delivered by the policy, they may also be used to evaluate a policy as the exposure. Matthay, E.C., Glymour, M.M. Causal Inference Challenges and New Directions for Epidemiologic Research on the Health Effects of Social Policies.
[205] Causal inference with observational data: the need for triangulation of ... — Counterfactual mediation Mediation approach based on conceptualizing ‘potential outcomes’ for each individual [Y(x)] that would have been observed if particular conditions were met (i.e. had the exposure X been set to the value x through some intervention) – regardless of the conditions that were in fact met for each individual; allows the presence of an interaction between the exposure and mediator to be tested, inclusion of binary mediators and outcomes, and sensitivity analyses to examine potential impact on conclusions of unmeasured confounding and measurement bias Main assumptions include conditional exchangeability, no interference and consistency; see de Stavola and colleagues (De Stavola, Daniel, Ploubidis, & Micali, 2015) for an accessible description of these assumptions and a comparison to assumptions made when estimating mediation within an SEM framework Still subject to the same threats to causality as traditional approaches to mediation analyses (including poorly measured or unmeasured confounding and measurement error); challenging to extend to examine individual paths via multiple mediators; each specific counterfactual mediation method subject to its own limitations – see VanderWeele (VanderWeele, 2015) Using a sequential counterfactual mediation approach, Aitken and colleagues (Aitken, Simpson, Gurrin, Bentley, & Kavanagh, 2018) showed that behavioural factors (including smoking and alcohol consumption) explained a further 5% of the association between disability acquisition and poor mental health in adults after accounting for material and psychosocial factors.
[206] Methods in causal inference. Part 3: measurement error and external ... — Satisfying the positivity assumption is a necesary condition for valid causal inference. Ensure that the measures relate to the scientific questions at hand - ensure that the data collected and the measures used directly relate to the research question to hand.
[208] Causal assumptions and causal inference in ecological experiments — Ecologists are familiar with good experimental design practices like randomizing treatments and having multiple replicates. However, even well-designed experiments rely on assumptions that, when left unexamined, can lead to inaccurate estimates of the causal effect of interest. Before exploring these assumptions, we introduce terms and concepts of causality using the potential outcomes
[211] PDF — This may be due to some of the challenges and limitations of ILSA data. First, the data available are limited to what can be collected comparably across countries. ... Education policy research is typically interested in asking causal questions about the impacts of policies on student outcomes and experiences. But attempting to ... validity. In
[212] The Analysis of International Large-Scale Assessments to Address Causal ... — As in any cross-national policy research, the validity of causal research using ILSA data relies on the assumptions that (1) the causal question and research design is equally valid in all countries
[213] The limits of inference: reassessing causality in international ... — In this paper we aim to scrutinize claims of causality—a concept of paramount importance in both the social sciences (Murnane & Willett, 2010; Russo, 2009; Shadish et al., 2002), public policy (Athey & Imbens, 2017; Stone, 1989) and statistics (Holland, 1986)—within the context of ILSAs. Specifically, this paper challenges the use of ILSA data to draw causal inferences, because we contend that it overlooks the fundamental limitations and assumptions inherent in ILSA data. For example, testing organizations like the International Association for the Evaluation of Educational Achievement (IEA) offer quasi-experimental design workshops that encourage the research and policy community to design studies aimed at making causal claims with ILSA data (Kennedy et al., 2023).
[214] PDF — Despite a precedent for avoiding causal claims with ILSA data, in recent years there has been a growing interest in doing so (Cordero et al., 2018; Komatsu & Rappleye, 2021). Recent initiatives reflect this trend; for example, the European Commission funded a project aimed at making causal claims using ILSA data (European Commission, 2018).
[226] Methods of Causal Analysis with ILSA Data | SpringerLink — The problem of unobserved heterogeneity is a major challenge to valid causal inference from ILSA data. Unobserved heterogeneity refers to differences between observational units (e.g., countries) with respect to variables, which are correlated with the independent and dependent variables under study. If these differences are not controlled for
[227] (PDF) Causal inferences with large scale assessment data: using a ... — The correlational nature of the ILSA data, resulting from the cross-sectional study design, may be another issue that could exclude these data from meta-analyses in education, especially when
[229] Evaluating the Impact of Unmeasured Confounding with ... - Value in Health — The quantitative assessment of the potential influence of unmeasured confounders in the analysis of observational data is rare, despite reliance on the "no unmeasured confounders" assumption. In a recent comparison of costs of care between two treatments for type 2 diabetes using a health care claims database, propensity score matching was implemented to adjust for selection bias though it
[230] Evaluating the Impact of Unmeasured Confounding with Internal ... — The lack of unmeasured confounders is a critical assumption underpinning the validity of comparative observational research. However, the quantitative assessment of the potential impact of unmeasured confounding is rarely reported. In this research, we have summarized and expanded on some of the existing methods for addressing unmeasured
[231] Application of methodological strategies to address unmeasured ... — Application of methodological strategies to address unmeasured confounding in real-world vaccine safety and effectiveness study: a systematic review - ScienceDirect Application of methodological strategies to address unmeasured confounding in real-world vaccine safety and effectiveness study: a systematic review Real-world vaccine safety and effectiveness evaluations are often challenged by unmeasured confounding, we conducted a systematic review on the current application of methodological strategies to address unmeasured confound in vaccine safety and effectiveness studies. (2) A total of 33 studies performed unmeasured confounding detection/quantification, including negative control (n=22) and E-value (n=13); In this systematic review, we identified only 42 studies that performed unmeasured confounding correction or confounding detection/quantification out of 913 included studies on vaccine safety and effectiveness.
[232] Sensitivity analysis of unmeasured confounding in causal inference ... — However, this assumption is usually questionable in observational studies, and the unmeasured confounding is one of the fundamental challenges in causal inference. If unmeasured confounders exist, the strong ignorability assumption is violated, which may result in a biased treatment effect estimation and undermine the validity and credibility
[233] [1203.3504] On Measurement Bias in Causal Inference - arXiv.org — This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias-free effect estimates in such models.
[236] PDF — The methodological literature on measurement error is also immense (see Carroll et al. 2006), and yet statisti-cians and econometricians are only beginning to address measurement error problems explicitly in the formal sta-tistical framework of causal inference (e.g., Lewbel 2007).
[242] Foundations and Future Directions for Causal Inference in Ecological ... — causal inference with synthesis science and meta-analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference. 1 | Introduction Questions about causal relationships are common in ecology: we
[246] Machine learning in causal inference for epidemiology — Machine learning in causal inference for epidemiology Eur J Epidemiol. 2024 Oct;39(10):1097-1108. doi: 10.1007/s10654-024-01173-x. Epub 2024 Nov 13. Authors ... Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form
[247] Towards Robust Causal Inference in Epidemiological Research: Employing ... — This approach uniquely combines the precision of correctly specified models with the versatility of data-adaptive, flexible machine learning algorithms. Despite its effectiveness, TMLE's integration of complex algorithms can introduce bias and under-coverage.
[248] PDF — Machine learning in causal inference for epidemiology increasing the number of parameters relaxes these con-straints, affording more flexibility and guarding against bias from model misspecification. However, this flexibility can lead to wider confidence intervals, reflecting increased vari-ance . Regularisation methods such as lasso, ridge
[249] Causal Structure Learning of High-Dimensional Data Based on Local and ... — Causal learning in high-dimensional data environments faces challenges such as extracting complex causal relationships, dimensional explosion, and high computational complexity. Traditional causal discovery methods are divided into local and global approaches. Local methods are computationally efficient and suitable for capturing complex local structures but are not applicable to high
[250] A Review of Causal Methods for High-Dimensional Data — Causal learning from observational data is an important scientific endeavor, but the statistical and computational challenges posed by the high-dimensionality of many modern datasets are substantial. Peculiarities such as spurious correlations, endogeneity, noise accumulation, and deflated empirical covariance estimation complicate analysis. These issues may lead to confounding bias, which can
[255] Foundations and Future Directions for Causal Inference in Ecological ... — Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers.