Retsef Levi, Elisabeth Paulson, Georgia Perakis, Emily Zhang
arXiv 9 Jun 2024 · Statistics — Machine Learning
arXiv:2406.05633 · PDF · DOI · OpenAlex · Extracted main text
We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that first partitions observations into disjoint clusters with similar treatment effects using a regression tree, and then leverages the (assumed) low-rank structure of the panel data to estimate the average treatment effect for each cluster. Our theoretical results establish the convergence of the resulting estimates to the true treatment effects. Computation experiments with semi-synthetic data show that our method achieves superior accuracy compared to alternative approaches, using a regression tree with no more than 40 leaves. Hence, our method provides more accurate and interpretable estimates than alternative methods.
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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 | Farias, V., Li, A., and Peng, T (2021) Learning treatment effects in panels with general intervention patterns | 0.928 | 25 | 6 | 80% |
| 2 | Wager, S. and Athey, S (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.874 | 5 | 2 | 100% |
| 3 | Foster, D. J. and Syrgkanis, V (2023) Orthogonal statistical learning | 0.644 | 2 | 2 | 100% |
| 4 | Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.644 | 2 | 2 | 100% |
| 5 | Athey, S., Tibshirani, J., and Wager, S (2019) Generalized random forests | 0.644 | 2 | 2 | 100% |
| 6 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2017) Double/debiased machine learning for treatment and causal parameters | 0.644 | 2 | 2 | 100% |
| 7 | Chernozhukov, V., Goldman, M., Semenova, V., and Taddy, M (2017) Orthogonal machine learning for demand estimation: High dimensional causal inference in dynamic panels | 0.644 | 2 | 2 | 100% |
| 8 | Chen, Y., Chi, Y., Fan, J., Ma, C., and Yan, Y (2020) Noisy matrix completion: Understanding statistical guarantees for convex relaxation via nonconvex optimization | 0.511 | 2 | 2 | 50% |
| 9 | Abadie, A. and Gardeazabal, J (2003) The economic costs of conflict: A case study of the basque country | 0.405 | 1 | 1 | 100% |
| 10 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 36 scored citations.