Bayesian Sensitivity Analyses for Policy Evaluation with Difference-in-Differences under Violations of Parallel Trends
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911092071989248 |
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| author | Han, Seong Woo Mitra, Nandita Hettinger, Gary Oganisian, Arman |
| author_facet | Han, Seong Woo Mitra, Nandita Hettinger, Gary Oganisian, Arman |
| contents | Violations of the parallel trends assumption pose significant challenges for causal inference in difference-in-differences (DiD) studies, especially in policy evaluations where pre-treatment dynamics and external shocks may bias estimates. In this work, we propose a Bayesian DiD framework to allow us to estimate the effect of policies when parallel trends is violated. To address potential deviations from the parallel trends assumption, we introduce a formal sensitivity parameter representing the extent of the violation, specify an autoregressive AR(1) prior on this term to robustly model temporal correlation, and explore a range of prior specifications - including fixed, fully Bayesian, and empirical Bayes (EB) approaches calibrated from pre-treatment data. By systematically comparing posterior treatment effect estimates across prior configurations when evaluating Philadelphia's sweetened beverage tax using Baltimore as a control, we show how Bayesian sensitivity analyses support robust and interpretable policy conclusions under violations of parallel trends. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02970 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Sensitivity Analyses for Policy Evaluation with Difference-in-Differences under Violations of Parallel Trends Han, Seong Woo Mitra, Nandita Hettinger, Gary Oganisian, Arman Methodology Violations of the parallel trends assumption pose significant challenges for causal inference in difference-in-differences (DiD) studies, especially in policy evaluations where pre-treatment dynamics and external shocks may bias estimates. In this work, we propose a Bayesian DiD framework to allow us to estimate the effect of policies when parallel trends is violated. To address potential deviations from the parallel trends assumption, we introduce a formal sensitivity parameter representing the extent of the violation, specify an autoregressive AR(1) prior on this term to robustly model temporal correlation, and explore a range of prior specifications - including fixed, fully Bayesian, and empirical Bayes (EB) approaches calibrated from pre-treatment data. By systematically comparing posterior treatment effect estimates across prior configurations when evaluating Philadelphia's sweetened beverage tax using Baltimore as a control, we show how Bayesian sensitivity analyses support robust and interpretable policy conclusions under violations of parallel trends. |
| title | Bayesian Sensitivity Analyses for Policy Evaluation with Difference-in-Differences under Violations of Parallel Trends |
| topic | Methodology |
| url | https://arxiv.org/abs/2508.02970 |