Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies

TLDR

Identifying causal mechanisms is a key goal in social science, yet existing statistical methods rely on untestable assumptions and are often inadequate, making the study of mechanisms both challenging and essential. The study seeks to advance causal mechanism research by offering three contributions. The authors develop a general algorithm for estimating causal mediation effects under a minimal set of assumptions, propose a sensitivity analysis method for key assumption violations, and suggest alternative designs that require weaker assumptions. The approach is demonstrated with media framing experiments and incumbency advantage studies.

Abstract

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.