Phillip Heiler, Asbjørn Kaufmann, Bezirgen Veliyev
arXiv 15 Dec 2024 · Econometrics
arXiv:2412.11179 · PDF · DOI · OpenAlex · Extracted main text
This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most importantly, we allow for a large share of units whose selection is indifferent to treatment, e.g. due to non-compliance. The existence of such a population is crucially tied to the regularity of sharp population bounds and thus conventional asymptotic inference for methods such as Lee bounds can be misleading. It can be solved using smoothed outer identification regions for inference. We provide semiparametrically efficient debiased machine learning estimators for both regular and smooth bounds that can accommodate high-dimensional covariates and flexible functional forms. Our study of active labor market policy reveals the empirical prevalence of the aforementioned indifference population and supports results from previous impact analysis under much weaker assumptions.
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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 | Heiler, P (2024) Heterogeneous treatment effect bounds under sample selection with an application to the effects of social media on political pol… self | 1.000 | 14 | 5 | 100% |
| 2 | Lee, D. S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.937 | 17 | 5 | 82% |
| 3 | Semenova, V (2024) Generalized Lee bounds | 0.934 | 27 | 8 | 81% |
| 4 | Hahn, J (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects | 0.928 | 5 | 5 | 80% |
| 5 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 5 | 3 | 80% |
| 6 | Zhang, J. L. and Rubin, D. B (2003) death | 0.928 | 4 | 3 | 100% |
| 7 | Schochet, P. Z., Burghardt, J., and McConnell, S (2008) Does job corps work? impact findings from the national job corps study | 0.874 | 5 | 2 | 100% |
| 8 | Huber, M. and Mellace, G (2015) Sharp bounds on causal effects under sample selection | 0.843 | 3 | 3 | 100% |
| 9 | Imbens, G. W. and Manski, C. F (2004) Confidence intervals for partially identified parameters | 0.811 | 4 | 2 | 100% |
| 10 | Burghardt, J., McConnell, S., Meckstroth, A., Shochet, P., Johnson,… (1999) National job corps study: Report on study implementation | 0.737 | 3 | 3 | 67% |
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 | Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity | 0.916 | 13 | 6 |
| 2 | Testing Exclusion and Shape Restrictions in Potential Outcomes Models | 0.644 | 2 | 2 |
| 3 | Adaptive Estimation of Aggregated Values of Conditional Linear Programs | 0.644 | 2 | 2 |