arXiv 27 Nov 2023 · Econometrics
arXiv:2311.15871 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates how certain relationship between observed and counterfactual distributions serves as an identifying condition for treatment effects when the treatment is endogenous, and shows that this condition holds in a range of nonparametric models for treatment effects. To this end, we first provide a novel characterization of the prevalent assumption restricting treatment heterogeneity in the literature, namely rank similarity. Our characterization demonstrates the stringency of this assumption and allows us to relax it in an economically meaningful way, resulting in our identifying condition. It also justifies the quest of richer exogenous variations in the data (e.g., multi-valued or multiple instrumental variables) in exchange for weaker identifying conditions. The primary goal of this investigation is to provide empirical researchers with tools that are robust and easy to implement but still yield tight policy evaluations.
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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 | Chernozhukov, V. and C. Hansen (2005) An IV model of quantile treatment effects | 1.000 | 7 | 5 | 100% |
| 2 | Vuong, Q. and H. Xu (2017) Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity | 0.843 | 3 | 3 | 100% |
| 3 | Imbens, G. W. and J. D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.811 | 4 | 2 | 100% |
| 4 | Calafiore, G. and M. C. Campi (2005) Uncertain convex programs: randomized solutions and confidence levels | 0.737 | 3 | 2 | 100% |
| 5 | Abadie, A., J. Angrist, and G. Imbens (2002) Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings | 0.644 | 2 | 2 | 100% |
| 6 | Dong, Y. and S. Shen (2018) Testing for rank invariance or similarity in program evaluation | 0.644 | 2 | 2 | 100% |
| 7 | Han, S. and S. Yang (2023) A Computational Approach to Identification of Treatment Effects for Policy Evaluation self | 0.644 | 2 | 2 | 100% |
| 8 | Kim, J. H. and B. G. Park (2022) Testing rank similarity in the local average treatment effects model | 0.644 | 2 | 2 | 100% |
| 9 | Mogstad, M., A. Torgovitsky, and C. R. Walters (2021) The causal interpretation of two-stage least squares with multiple instrumental variables | 0.511 | 2 | 1 | 100% |
| 10 | Chesher, A (2005) Nonparametric identification under discrete variation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Distributional Treatment Effect with Latent Rank Invariance | 0.405 | 1 | 1 |