Choosing the Causal Estimand for Propensity Score Analysis of Observational Studies

TL;DR

This paper guides medical researchers in selecting appropriate causal estimands for propensity score analysis in observational studies, emphasizing correct interpretation and implications for valid, replicable results.

Contribution

It provides comprehensive guidance on choosing and interpreting estimands like ATE, ATT, ATU, and ATO in observational causal inference.

Findings

Clarifies assumptions and interpretations of different estimands.

Highlights importance of estimand choice for valid causal inference.

Discusses implications for regression and instrumental variable analyses.

Abstract

Matching and weighting methods for observational studies involve the choice of an estimand, the causal effect with reference to a specific target population. Commonly used estimands include the average treatment effect in the treated (ATT), the average treatment effect in the untreated (ATU), the average treatment effect in the population (ATE), and the average treatment effect in the overlap (i.e., equipoise population; ATO). Each estimand has its own assumptions, interpretation, and statistical methods that can be used to estimate it. This article provides guidance on selecting and interpreting an estimand to help medical researchers correctly implement statistical methods used to estimate causal effects in observational studies and to help audiences correctly interpret the results and limitations of these studies. The interpretations of the estimands resulting from regression and instrumental variable analyses are also discussed. Choosing an estimand carefully is essential for making valid inferences from the analysis of observational data and ensuring results are replicable and useful for practitioners.