arXiv 4 Oct 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2024)
arXiv:2210.01938 · PDF · DOI · OpenAlex · Extracted main text
This paper identifies the probability of causation when there is sample selection. We show that the probability of causation is partially identified for individuals who are always observed regardless of treatment status and derive sharp bounds under three increasingly restrictive sets of assumptions. The first set imposes an exogenous treatment and a monotone sample selection mechanism. To tighten these bounds, the second set also imposes the monotone treatment response assumption, while the third set additionally imposes a stochastic dominance assumption. Finally, we use experimental data from the Colombian job training program J\'ovenes en Acci\'on to empirically illustrate our approach's usefulness. We find that, among always-employed women, at least 10.2% and at most 13.4% transitioned to the formal labor market because of the program. However, our 90%-confidence region does not reject the null hypothesis that the lower bound is equal to zero.
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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 | Attanasio, O., A. Kugler, and C. Meghir (2011) Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial | 1.000 | 8 | 3 | 100% |
| 2 | Pearl, J (1999) Probabilities of Causation: Three Counterfactual Interpretations and their Identification | 0.950 | 7 | 3 | 86% |
| 3 | Tian, J. and J. Pearl (2000) Probabilities of Causation: Bounds and Identification | 0.950 | 7 | 3 | 86% |
| 4 | Lee, D. S (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 0.909 | 12 | 4 | 75% |
| 5 | Jun, S. J. and S. Lee (2022, December) (2022) Identifying the Effect of Persuasion | 0.899 | 11 | 4 | 73% |
| 6 | Chernozhukov, V., S. Lee, and A. M. Rosen (2013) Intersection Bounds: Estimation and Inference | 0.874 | 12 | 4 | 67% |
| 7 | Cinelli, C. and J. Pearl (2021) Generalizing Experimental Results by Leveraging Knowledge of Mechanisms | 0.874 | 6 | 2 | 100% |
| 8 | Attanasio, O., A. Guarin, C. Medina, and C. Meghir (2017) Vocational Training for Disadvantaged Youth in Colombia: A Long-Term Follow-Up | 0.874 | 5 | 2 | 100% |
| 9 | Chen, X. and C. A. Flores (2015) Bounds on Treatment Effects in the Presence of Sample Selection and Noncompliance: The Wage Effects of Job Corps | 0.843 | 4 | 3 | 75% |
| 10 | Heckman, J. J., J. Smith, and N. Clements (1997) Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts | 0.830 | 7 | 3 | 57% |
Showing the top 10 of 31 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2503.06046 | 0.737 | 3 | 3 |
| 2 | 2410.14871 | 0.511 | 2 | 1 |
| 3 | Estimating the Intensive Margin Effect in Panel Data Settings | 0.405 | 1 | 1 |
| 4 | 2509.26517 | 0.405 | 1 | 1 |