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On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units

Luis Alvarez, Bruno Ferman

arXiv 17 Jun 2025 · Econometrics

arXiv:2506.14998 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We study how inference methods for settings with few treated units that rely on treatment effect homogeneity extend to alternative inferential targets when treatment effects are heterogeneous -- namely, tests of sharp null hypotheses, inference on realized treatment effects, and prediction intervals. We show that inference methods for these alternative targets are deeply interconnected: they are either equivalent or become equivalent under additional assumptions. Our results show that methods designed under treatment effect homogeneity can remain valid for these alternative targets when treatment effects are stochastic, offering new theoretical justifications and insights on their applicability.

Citation extraction

36
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86
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distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Ferman, B. and Pinto, C (2019) Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity self1.000123100%
2Alvarez, L., Ferman, B., and Wüthrich, K (2025) Inference with few treated units self1.00093100%
3Cattaneo, M. D., Feng, Y., and Titiunik, R (2021) Prediction intervals for synthetic control methods0.92843100%
4Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) An exact and robust conformal inference method for counterfactual and synthetic controls0.92843100%
5Conley, T. G. and Taber, C. R (2011) Inference with "difference in differences" with a small number of policy changes0.87482100%
6Alvarez, L. and Ferman, B (2023) Inference in difference-in-differences with few treated units and spatial correlation self0.81142100%
7Bugni, F. A., Canay, I. A., and Shaikh, A. M (2018) Inference under covariate-adaptive randomization0.64422100%
8Lei, L. and Candès, E. J (2021) Conformal Inference of Counterfactuals and Individual Treatment Effects0.64422100%
9Chung, E. and Olivares, M (2021) Permutation test for heterogeneous treatment effects with a nuisance parameter0.64422100%
10Lei, J., Robins, J., and Wasserman, L (2013) Distribution-free prediction sets0.64422100%

Showing the top 10 of 36 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference with few treated units0.69351