Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei, Xiaoman Luo
arXiv 29 Jul 2021 · Econometrics · publishedQuantitative Economics (2024) · 14 citations (OpenAlex)
arXiv:2107.13737 · PDF · DOI · OpenAlex · Extracted main text
We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two-way-fixed-effects specification with unit-specific weights that arise from a model for the assignment mechanism. We show how to construct these weights in various settings, including the staggered adoption setting, where units opt into the treatment sequentially but permanently. The resulting estimator converges to an average (over units and time) treatment effect under the correct specification of the assignment model, even if the fixed effect model is misspecified. We show that our estimator is more robust than the conventional two-way estimator: it remains consistent if either the assignment mechanism or the two-way regression model is correctly specified. In addition, the proposed estimator performs better than the two-way-fixed-effect estimator if the outcome model and assignment mechanism are locally misspecified. This strong double robustness property underlines and quantifies the benefits of modeling the assignment process and motivates using our estimator in practice. We also discuss an extension of our estimator to handle dynamic treatment effects.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Susan Athey and Guido W Imbens (2022) Design-based analysis in difference-in-differences settings with staggered adoption self | 0.874 | 8 | 2 | 100% |
| 2 | Jonathan Roth and Pedro HC Sant’Anna (2023) Efficient estimation for staggered rollout designs | 0.874 | 5 | 2 | 100% |
| 3 | Iavor Bojinov, Ashesh Rambachan, and Neil Shephard (2021) Panel experiments and dynamic causal effects: A finite population perspective | 0.811 | 4 | 2 | 100% |
| 4 | Dmitry Arkhangelsky and Guido W Imbens (2022) Doubly robust identification for causal panel data models self | 0.737 | 3 | 2 | 100% |
| 5 | Brantly Callaway and Pedro HC Sant’Anna (2021) Difference-in-differences with multiple time periods | 0.737 | 3 | 2 | 100% |
| 6 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 7 | Clement De Chaisemartin and Xavier d'Haultfoeuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 8 | Andrew Goodman-Bacon (2021) Difference-in-differences with variation in treatment timing | 0.737 | 3 | 2 | 100% |
| 9 | Joseph Kang and Joseph Schafer (2007) Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data | 0.737 | 3 | 2 | 100% |
| 10 | James M Robins, Andrea Rotnitzky, and Lue Ping Zhao (1994) Estimation of regression coefficients when some regressors are not always observed | 0.737 | 3 | 2 | 100% |
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