Clément de Chaisemartin, Antoine Deeb
arXiv 27 May 2024 · Econometrics
arXiv:2405.17254 · PDF · DOI · OpenAlex · Extracted main text
In multi-site randomized trials with many sites and few randomization units per site, an Empirical-Bayes estimator can be used to estimate the variance of the treatment effect across sites. When this estimator indicates that treatment effects do vary, we propose estimators of the coefficients from regressions of site-level effects on site-level characteristics that are unobserved but can be unbiasedly estimated, such as sites' average outcome without treatment, or site-specific treatment effects on mediator variables. In experiments with imperfect compliance, we show that the sign of the correlation between local average treatment effects (LATEs) and site-level characteristics is identified, and we propose a partly testable assumption under which the variance of LATEs is identified. We use our results to revisit Behaghel et al (2014), who study the effect of counseling programs on job seekers' job-finding rate, in 200 job placement agencies in France. We find considerable treatment-effect heterogeneity, both for intention to treat and LATE effects, and the treatment effect is negatively correlated with sites' job-finding rate without treatment.
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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 | Behaghel, L., B. Crépon, and M. Gurgand (2014) Private and public provision of counseling to job seekers: Evidence from a large controlled experiment | 0.693 | 15 | 1 | 100% |
| 2 | Li, X. and P. Ding (2017) General forms of finite population central limit theorems with applications to causal inference | 0.693 | 5 | 1 | 100% |
| 3 | Liu, R. Y. et al (1988) Bootstrap procedures under some non-iid models | 0.693 | 5 | 1 | 100% |
| 4 | Walters, C. R (2015) Inputs in the production of early childhood human capital: Evidence from head start | 0.585 | 3 | 1 | 100% |
| 5 | De Chaisemartin, C. and X. d’Haultfoeuille (2018) Fuzzy differences-in-differences | 0.511 | 2 | 1 | 100% |
| 6 | Imbens, G. W. and D. B. Rubin (2015) Causal inference in statistics, social, and biomedical sciences | 0.511 | 2 | 1 | 100% |
| 7 | Kline, P., R. Saggio, and M. Slvsten (2020) Leave-out estimation of variance components | 0.511 | 2 | 1 | 100% |
| 8 | Loh, P.-L. and M. J. Wainwright (2011) High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity | 0.511 | 2 | 1 | 100% |
| 9 | Morris, C. N (1983) Parametric empirical bayes inference: theory and applications | 0.511 | 2 | 1 | 100% |
| 10 | Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.