Characterizing the Effects of Environmental Exposures on Social Mobility: Bayesian Semi-parametrics for Principal Stratification

2024-11-30

AI Summary

This study investigates how PM₂.₅ exposure affects social mobility in the United States through educational attainment and identifies underlying causal pathways. Addressing bias from education—a post-treatment confounder—in conventional causal inference, we propose the first application of the dependent Dirichlet process (DDP) within the principal stratification framework to flexibly model latent potential outcome distributions nonparametrically, simultaneously adjusting for both pre-treatment confounding and post-treatment bias. We develop a Bayesian semiparametric DDP mixture model and validate its performance via Monte Carlo simulations, demonstrating substantial improvements in missing-data imputation accuracy and treatment effect estimation. Empirically, we find significant heterogeneous suppression of social mobility among low-education subpopulations attributable to PM₂.₅ exposure. Our primary contribution is a novel causal inference framework integrating DDP with principal stratification—offering a methodological advance for interdisciplinary research at the intersection of environmental health and social inequality.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Principal stratification provides a robust causal inference framework for the adjustment of post-treatment variables when comparing the effects of a treatment in health and social sciences. In this paper, we introduce a novel Bayesian nonparametric model for principal stratification, leveraging the dependent Dirichlet process to flexibly model the distribution of potential outcomes. By incorporating confounders and potential outcomes for the post-treatment variable in the Bayesian mixture model for the final outcome, our approach improves the accuracy of missing data imputation and allows for the characterization of treatment effects across strata defined based on the values of the post-treatment variable. We assess the performance of our method through a Monte Carlo simulation study where we compare the proposed method with state-of-the-art Bayesian method in principal stratification. Finally, we leverage the proposed method to evaluate the principal causal effects of exposure to air pollution on social mobility in the US on strata defined by educational attainment.

Problem

• Investigating causal effects of PM2.5 exposure on social mobility
• Examining educational attainment as mediator between pollution and mobility
• Developing Bayesian semi-parametric method for causal pathway analysis

Innovation

• Bayesian semi-parametric method for causal inference
• Infinite mixtures model primary outcome distribution
• Principal stratification estimates post-treatment variable effects