Causal inference for multiple continuous exposures via the multivariate generalized propensity score

TL;DR

This paper introduces a multivariate generalized propensity score (mvGPS) method to estimate the joint causal effects of multiple continuous exposures on an outcome, addressing a gap in existing single-exposure GPS techniques.

Contribution

The paper proposes a novel mvGPS approach that models multiple continuous exposures simultaneously, enabling estimation of a dose-response surface for joint exposures.

Findings

mvGPS achieves better confounder balance across exposures

Reduces bias in treatment effect estimates in simulations

Successfully applied to childhood obesity intervention data

Abstract

The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (e.g., dose of medication or years of education). Current GPS methods allow estimation of the dose-response relationship between a single continuous exposure and an outcome. However, in many real-world settings, there are multiple exposures occurring simultaneously that could be causally related to the outcome. We propose a multivariate GPS method (mvGPS) that allows estimation of a dose-response surface that relates the joint distribution of multiple continuous exposure variables to an outcome. The method involves generating weights under a multivariate normality assumption on the exposure variables. Focusing on scenarios with two exposure variables, we show via simulation that the mvGPS method can achieve balance across sets of confounders that may differ for different exposure variables and reduces bias of the treatment effect estimates under a variety of data generating scenarios. We apply the mvGPS method to an analysis of the joint effect of two types of intervention strategies to reduce childhood obesity rates.