From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity

2026-07-02

AI Summary

Traditional heterogeneity analyses rely on pre-specified subgroups, limiting their ability to uncover complex effect modification mechanisms, while existing data-driven approaches often lack interpretability. This study proposes a novel paradigm grounded in causal transportability, treating population composition as a continuous variable to model the relationship between effect modifier distributions and the overall exposure effect. The framework enables estimation of intervention effects across diverse populations and identification of key vulnerability features. It integrates causal transportability, effect modifier selection—combining prior knowledge with data-adaptive strategies—and effect surface modeling, facilitating both effect attribution and ranking of modifier importance. Empirical application to child stunting and drought exposure successfully identifies critical modifiers, and an open-source Shiny interactive tool is provided to support broad adoption.

Abstract

Identifying heterogeneous populations across which exposure effects vary is essential for transportability applications, cost-benefit analyses, and intervention prioritization. Traditional methods for heterogeneity analyses rely on parametric regression with prespecified subgroups, which may fail to capture complex patterns of effect modification. While recent data-adaptive methods improve high-dimensional heterogeneous effect prediction, they add methodological complexity to analyses and may offer limited insight into key drivers of heterogeneity. In this paper, we propose a novel, conceptual approach for heterogeneity analyses that considers how exposure effects would differ in populations with different compositions by modeling the population-level effect surface as a function of the distribution of effect modifiers. The approach consists of three steps: i) selecting confounders and effect modifiers based on prior knowledge (or alternatively using data-adaptive methods to learn effect modifiers), ii) estimating exposure effects in hypothetical populations with different effect modifier prevalences using transportability methods, and iii) modeling the estimated effects as a function of prevalence values. This approach provides two types of outputs: estimation of the change in the population-level exposure effects attributable to increases in effect modifier prevalence and ranking of effect estimates across multiple effect modifiers and prevalences to identify population characteristics most strongly associated with differential vulnerability. We demonstrate the approach using Demographic and Health Surveys data to examine heterogeneous effects of drought on child stunting and provide a Shiny application to implement this approach in any setting.

Problem

• effect heterogeneity
• transportability
• population composition
• effect modifiers
• exposure effects

Innovation

• effect heterogeneity
• transportability
• effect modifiers
• population composition
• causal inference