2021/06/19 by Noah Greifer, Elizabeth A. Stuart, Greifer, Noah +1 · 5 voices · 34 citations
Mathematics · Medicine · Psychology · #Advanced Causal Inference Techniques #Average treatment effect #Causal inference #Demography #Econometrics #Mathematics #Medicine #Observational study #Population #Propensity score matching #Psychology #Sociology #Statistics #Weighting #stat.ME
paper · pdf · doi:10.48550/arxiv.2106.10577
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/06/19 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05
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.