A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption
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Wiley
2025
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| author | Yi Liu Yuan Wang Ying Gao Tonia Poteat Roland A. Matsouaka |
| author_facet | Yi Liu Yuan Wang Ying Gao Tonia Poteat Roland A. Matsouaka Yi Liu Yuan Wang Ying Gao Tonia Poteat Roland A. Matsouaka |
| collection | Wiley Open Access |
| contents | A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption Yi Liu Yuan Wang Ying Gao Tonia Poteat Roland A. Matsouaka Statistics in Medicine 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. 10.1002/sim.70329 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/sim.70329 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70329 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
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| spellingShingle | A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption Yi Liu Yuan Wang Ying Gao Tonia Poteat Roland A. Matsouaka Statistics in Medicine A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption Yi Liu Yuan Wang Ying Gao Tonia Poteat Roland A. Matsouaka Statistics in Medicine 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. 10.1002/sim.70329 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70329 |