Mixed-effects Outcome-Adaptive Lasso for Propensity Score Estimation under Partial Interference

2026-08-17

AI Summary

This study addresses the issue of excessive variance in inverse probability weighting caused by extreme group-level propensity scores under partial interference. We propose an adaptive Lasso method based on mixed-effects logistic regression that simultaneously performs covariate selection and propensity score modeling. This approach effectively accounts for unobserved between-group heterogeneity and automatically identifies group-level confounders. Both theoretical analysis and simulation studies demonstrate that the proposed method possesses the oracle property and exhibits high efficiency in small samples. Furthermore, its practical utility is validated using malaria data from the Democratic Republic of the Congo, where it significantly enhances the stability and accuracy of causal effect estimation compared to existing methods.

Abstract

Interference occurs when one individual's treatment or exposure affects another individual's outcome. In particular, we assume partial interference, where individuals are divided into groups such that there is no interference between individuals in different groups. In observational studies, inverse probability weighting (IPW) based on propensity scores is often used for causal effect estimation. However, under partial interference, the group-level propensity score must be estimated, and it is more likely to take extreme values than the usual individual-level propensity score. As a result, IPW estimators may have large variances. This problem can become more serious when many covariates are available. In this study, we propose an Outcome-Adaptive Lasso based on a mixed-effects logistic regression model to stably estimate causal effects under partial interference. The proposed method performs covariate selection and estimation in the propensity score model simultaneously while accounting for unobserved group-level heterogeneity in treatment assignment. Under regularity conditions, we show that the proposed method has the oracle property and that the IPW estimators based on the proposed method are consistent and asymptotically normal. Through Monte Carlo simulations, we demonstrate that the proposed method tends to select confounders and prognostic factors at high frequencies, while excluding instrumental variables and spurious variables. The results further suggest that the proposed method improves the finite-sample efficiency of IPW estimators. We evaluate the performance of the proposed method using malaria data from the Democratic Republic of the Congo Demographic and Health Survey (DHS).

Problem

• Partial Interference
• Propensity Score Estimation
• Inverse Probability Weighting
• Causal Inference
• High-dimensional Covariates

Innovation

• Mixed-effects Outcome-Adaptive Lasso
• Partial Interference
• Propensity Score Estimation
• Oracle Property
• Inverse Probability Weighting