arXiv 14 Dec 2025 · Econometrics
arXiv:2512.12781 · PDF · DOI · OpenAlex · Extracted main text
Using only retrospective data, we propose an estimator for predicting the treatment effect for the same treatment/policy to be implemented in another location or time period, which requires no input from the target population. More specifically, we minimize the worst-case mean square error for the prediction of treatment effect within a class of distributions inside the Wasserstein ball centered on the source distribution. Since the joint distribution of potential outcomes is not identified, we pick the best and worst copulas of the marginal distributions of two potential outcomes as our optimistic and pessimistic optimization objects for partial identification. As a result, we can attain the upper and lower bounds of the minimax optimizer. The minimax solution differs depending on whether treatment effects are homogeneous or heterogeneous. We derive the consistency and asymptotic distribution of the bound estimators, provide a two-step inference procedure, and discuss the choice of the Wasserstein ball radius.
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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 | Hotz, V Joseph and Imbens, Guido W and Mortimer, Julie H (2005) Predicting the efficacy of future training programs using past experiences at other locations | 1.000 | 5 | 4 | 100% |
| 2 | Imbens, Guido W and Manski, Charles F (2004) Confidence intervals for partially identified parameters | 0.928 | 4 | 3 | 100% |
| 3 | Guo, Zijian (2024) Statistical inference for maximin effects: Identifying stable associations across multiple studies | 0.843 | 3 | 3 | 100% |
| 4 | Stoye, Jörg (2009) More on confidence intervals for partially identified parameters | 0.737 | 3 | 3 | 67% |
| 5 | Gao, Rui and Kleywegt, Anton (2023) Distributionally robust stochastic optimization with Wasserstein distance | 0.644 | 2 | 2 | 100% |
| 6 | Spini, Pietro Emilio (2021) Robustness, heterogeneous treatment effects and covariate shifts | 0.644 | 2 | 2 | 100% |
| 7 | Zhang, Yi and Huang, Melody and Imai, Kosuke (2024) Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data | 0.644 | 2 | 2 | 100% |
| 8 | Blanchet, Jose and Kang, Yang and Murthy, Karthyek (2019) Robust Wasserstein profile inference and applications to machine learning | 0.585 | 3 | 3 | 33% |
| 9 | van der Vaart, Aad W. and Jon A. Wellner (1996) Weak Convergence and Empirical Processes | 0.511 | 3 | 2 | 33% |
| 10 | Dehejia, Rajeev H and Wahba, Sadek (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | An econometrician's guide to optimal transport | 0.405 | 1 | 1 |