arXiv 19 Jul 2024 · Econometrics
arXiv:2407.14635 · PDF · DOI · OpenAlex · Extracted main text
Important questions for impact evaluation require knowledge not only of average effects, but of the distribution of treatment effects. The inability to observe individual counterfactuals makes answering these empirical questions challenging. I propose an inference approach for points of the distribution of treatment effects by incorporating predicted counterfactuals through covariate adjustment. I provide finite-sample valid inference using sample-splitting, and asymptotically valid inference using cross-fitting, under arguably weak conditions. Revisiting five randomized controlled trials on microcredit that reported null average effects, I find important distributional impacts, with some individuals helped and others harmed by the increased credit access.
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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 | Fan, Y. and S. S. Park (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.964 | 19 | 5 | 89% |
| 2 | Meager, R (2022) Aggregating distributional treatment effects: A Bayesian hierarchical analysis of the microcredit literature | 0.874 | 5 | 2 | 100% |
| 3 | Ji, W., L. Lei, and A. Spector (2023) Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects | 0.860 | 11 | 4 | 64% |
| 4 | Semenova, V (2023) Adaptive Estimation of Intersection Bounds: a Classification Approach | 0.843 | 10 | 4 | 60% |
| 5 | Massart, P (1990) The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality | 0.737 | 4 | 2 | 75% |
| 6 | Banerjee, A., D. Karlan, and J. Zinman (2015) b): Six randomized evaluations of microcredit: Introduction and further steps | 0.737 | 3 | 2 | 100% |
| 7 | Meager, R (2019) Understanding the average impact of microcredit expansions: A bayesian hierarchical analysis of seven randomized experiments | 0.737 | 3 | 2 | 100% |
| 8 | Stoye, J (2009) More on confidence intervals for partially identified parameters | 0.644 | 5 | 2 | 40% |
| 9 | Convergences (2023) Impact Finance Barometer 2023, https://www.convergences.org/en/barometre-de-la-finance-a-impact/, accessed: 2024-03-21 | 0.644 | 2 | 2 | 100% |
| 10 | Augsburg, B., R. De Haas, H. Harmgart, and C. Meghir (2015) The impacts of microcredit: Evidence from Bosnia and Herzegovina | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators | 0.405 | 1 | 1 |