Instrumental variable methods for causal inference

TLDR

Observational health studies aim to estimate causal treatment effects but are threatened by unmeasured confounding, which can bias results when randomization is infeasible. This tutorial outlines the causal effects estimable with instrumental variables, the assumptions and sensitivity analyses required, estimation methods, and potential sources of instruments in health research. Instrumental variable analysis mitigates unmeasured confounding by employing a variable that is independent of confounders, influences treatment assignment, and affects outcomes solely through its effect on treatment.

Abstract

A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.