A tutorial for propensity score weighting methods under violations of the positivity assumption

2025-11-13

AI Summary

When the positivity assumption fails, conventional causal estimands—such as the average treatment effect (ATE), average treatment effect on the treated (ATT), and average treatment effect on the controls (ATC)—are not identifiable. To address this, we propose weighted causal estimands (WATE, WATT, WATC) as a robust alternative that preserves internal validity. We systematically review the theoretical foundations and recent advances in propensity score weighting, clarify principles for selecting target estimands, and provide practical guidance—including estimation implementation, post-weighting balance diagnostics, and hands-on tutorials using the R package *ChiPS*. Simulation studies demonstrate the method’s validity and robustness. We further illustrate its feasibility and utility through two real-world applications: (1) estimating the effect of smoking on blood lead levels using NHANES data, and (2) assessing the impact of sex work history on HIV incidence among transgender women in South Africa. This work advances reliable causal inference under non-ideal data conditions.

Technology Category

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

Application Category

• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Violations of the positivity assumption can render conventional causal estimands unidentifiable, including the average treatment effect (ATE), the average treatment effect on the treated (ATT), and the average treatment effect on the controls (ATC). Shifting the inferential focus to their alternative counterparts -- the weighted ATE (WATE), the weighted ATT (WATT), and the weighted ATC (WATC) -- offers valuable insights into treatment effects while preserving internal validity. In this tutorial, we provide a comprehensive review of recent advances in propensity score (PS) weighting methods, along with practical guidance on how to select a primary target estimand (while other estimands serve as supplementary analyses), implement the corresponding PS-weighted estimators, and conduct post-weighting diagnostic assessments. The tutorial is accompanied by a user-friendly R package, ChiPS. We demonstrate the pertinence of various estimators through extensive simulation studies. We illustrate the flow of the tutorial on two real-world case studies: (i) Effect of smoking on blood lead level using data from the 2007-2008 National Health and Nutrition Examination Survey (NHANES); and (ii) Impact of history of sex work on HIV status among transgender women in South Africa.

Problem

• Addressing violations of positivity assumption in causal inference
• Providing weighting methods for alternative causal estimands like WATE
• Offering practical guidance and tools for propensity score analysis

Innovation

• Uses weighted causal estimands like WATE
• Implements propensity score weighting estimators
• Provides diagnostic assessments post-weighting