arXiv 6 Oct 2025 · Econometrics · 1 citations (OpenAlex)
arXiv:2510.05454 · PDF · DOI · OpenAlex · Extracted main text
Researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects. When treatment effects are heterogeneous, however, such specifications generally fail to recover this average effect. Augmenting these specifications with interaction terms between demeaned covariates and treatment eliminates this bias, but often leads to imprecise estimates and becomes infeasible under limited overlap. We propose a generalized ridge regression estimator, $regulaTE$, that penalizes the coefficients on the interaction terms to achieve an optimal trade-off between worst-case bias and variance in estimating the average effect under limited treatment effect heterogeneity. Building on this estimator, we construct confidence intervals that remain valid under limited overlap and can also be used to assess sensitivity to violations of the constant effects assumption. We illustrate the method in empirical applications under unconfoundedness and staggered adoption, providing a practical approach to inference under limited overlap.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Angrist, J. D (1998) Estimating the labor market impact of voluntary military service using social security data on military applicants | 1.000 | 11 | 3 | 100% |
| 2 | Armstrong, T. B. and M. Kolesár (2018) Optimal inference in a class of regression models | 0.843 | 4 | 3 | 75% |
| 3 | Angrist, J. D. and J.-S. Pischke (2009) Mostly harmless econometrics: An empiricist's companion | 0.811 | 4 | 2 | 100% |
| 4 | Armstrong, T. B., M. Kolesár, and S. Kwon (2023) Bias-aware inference in regularized regression models | 0.754 | 7 | 3 | 43% |
| 5 | Armstrong, T. B., P. Kline, and L. Sun (2025) Adapting to misspecification | 0.737 | 5 | 3 | 40% |
| 6 | Armstrong, T. B. and M. Kolesár (2021) Finite-sample optimal estimation and inference on average treatment effects under unconfoundedness | 0.737 | 3 | 2 | 100% |
| 7 | de Chaisemartin, C. and X. D'Haultfuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.737 | 3 | 2 | 100% |
| 8 | Goodman-Bacon, A (2021) Difference-in-differences with variation in treatment timing | 0.737 | 3 | 2 | 100% |
| 9 | Sanchez-Becerra, A (2023) Robust inference for the treatment effect variance in experiments using machine learning | 0.737 | 3 | 2 | 100% |
| 10 | Aizer, A., S. Eli, J. Ferrie, and A. Lleras-Muney (2016) The long-run impact of cash transfers to poor families | 0.693 | 5 | 1 | 100% |
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Robust Inference for Weighted Estimands | 0.511 | 2 | 1 |
| 2 | Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence | 0.405 | 1 | 1 |