Efficient semiparametric estimation of marginal treatment effects with genetic instrumental variables

2026-03-09

AI Summary

This study addresses the instability in estimating the causal effect of alcohol abuse on blood pressure, which arises from unobserved individual heterogeneity and a low compliance rate associated with genetic instrumental variables. Within the marginal treatment effect (MTE) framework, the paper introduces an efficient influence function for the first time, integrating semiparametric modeling with nonparametric propensity score estimation to substantially enhance robustness in the tails of the propensity score distribution. This approach effectively mitigates sampling uncertainty in these tail regions and reveals that individuals with the strongest propensity toward alcohol abuse exhibit the most pronounced increases in blood pressure, thereby confirming the presence of “adverse selection into treatment.” Compared to conventional methods, the proposed estimator demonstrates markedly improved precision and stability.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Alcohol misuse is a key target of public health strategies aimed at reducing cardiovascular risk. The effect of excessive alcohol consumption on blood pressure may vary systematically with individuals' unobserved propensity to engage in heavy drinking, complicating causal inference with observational data. The marginal treatment effects framework uses an instrumental variable for treatment choice (excessive alcohol consumption) to study how selection into treatment is linked with the treatment effect. We explore the use of a genetic instrument within this framework, which is challenging because genetic compliers (individuals for whom a change in the instrument changes their treatment choice) are likely to be a small proportion of the overall sample. This can lead to greater sampling uncertainty in the tails of the propensity score distribution, i.e., the conditional probability of choosing treatment, and in turn poor estimation of causal estimands that measure heterogeneous treatment effects. We show that the use of efficient influence functions of target estimands improves estimation in terms of robustness to sampling uncertainty in nonparametrically estimated propensity scores. We find evidence of reverse selection on gains: individuals most prone to excessive alcohol consumption experience larger adverse effects on blood pressure.

Problem

• marginal treatment effects
• genetic instrumental variables
• causal inference
• propensity score
• heterogeneous treatment effects

Innovation

• marginal treatment effects
• genetic instrumental variables
• efficient influence functions
• heterogeneous treatment effects
• propensity score