Effect heterogeneity and variable selection for standardizing causal effects to a target population

TL;DR

This paper explores different homogeneity conditions for standardizing causal effects across populations, comparing their assumptions, implications for covariate selection, and methods, including recent counterfactual approaches.

Contribution

It provides a comprehensive comparison of homogeneity conditions for effect standardization and discusses their implications for covariate selection and causal effect estimation.

Findings

Different homogeneity conditions have distinct implications for covariate selection.

Counterfactual approaches can avoid some traditional problems but may require extensive covariate adjustment.

The paper clarifies when and how standardization procedures yield unbiased effect estimates.

Abstract

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in unbiased estimates of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.