Luis Alvarez, Bruno Ferman, Kaspar Wüthrich
arXiv 28 Apr 2025 · Econometrics · 3 citations (OpenAlex)
arXiv:2504.19841 · PDF · DOI · OpenAlex · Extracted main text
In many causal inference applications, only one or a few units (or clusters of units) are treated. An important challenge in such settings is that standard inference methods that rely on asymptotic theory may be unreliable, even when the total number of units is large. This survey reviews and categorizes inference methods that are designed to accommodate few treated units, considering both cross-sectional and panel data methods. We discuss trade-offs and connections between different approaches. In doing so, we propose slight modifications to improve the finite-sample validity of some methods, and we also provide theoretical justifications for existing heuristic approaches that have been proposed in the literature.
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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 | Roth, J., Sant’Anna, P. H., Bilinski, A., and Poe, J (2023) What’s trending in difference-in-differences? a synthesis of the recent econometrics literature | 1.000 | 6 | 4 | 100% |
| 2 | Ferman, B. and Pinto, C (2019) Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity self | 0.874 | 13 | 2 | 100% |
| 3 | Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) An exact and robust conformal inference method for counterfactual and synthetic controls | 0.874 | 6 | 2 | 100% |
| 4 | Abadie, A., Athey, S., Imbens, G. W., and Wooldridge, J. M (2020) Sampling-based versus design-based uncertainty in regression analysis | 0.874 | 5 | 2 | 100% |
| 5 | Goncalves, S. and Ng, S (2024) Imputation of counterfactual outcomes when the errors are predictable | 0.874 | 5 | 2 | 100% |
| 6 | Conley, T. G. and Taber, C. R (2011) Inference with "difference in differences" with a small number of policy changes | 0.843 | 35 | 5 | 60% |
| 7 | de Chaisemartin, C. and D’Haultfoeuille, X (2022) Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey | 0.843 | 3 | 3 | 100% |
| 8 | MacKinnon, J. G. and Webb, M. D (2020) Randomization inference for difference-in-differences with few treated clusters | 0.830 | 7 | 3 | 57% |
| 9 | Leung, M. P (2022) Dependence-robust inference using resampled statistics | 0.830 | 7 | 2 | 86% |
| 10 | Canay, I. A., Romano, J. P., and Shaikh, A. M (2017) Randomization tests under an approximate symmetry assumption | 0.817 | 11 | 3 | 55% |
Showing the top 10 of 134 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units | 1.000 | 9 | 3 |
| 2 | ASSESSING INFERENCE METHODS | 0.644 | 4 | 1 |
| 3 | Cluster-robust inference with a single treated cluster using the t-test | 0.405 | 1 | 1 |
| 4 | 2604.27187 | 0.405 | 1 | 1 |