arXiv 16 Nov 2025 · Econometrics
arXiv:2511.12640 · PDF · DOI · OpenAlex · Extracted main text
We propose plug-in (PI) and double machine learning (DML) estimators of average treatment effect (ATE), average treatment effect on the treated (ATET) and local average treatment effect (LATE) in the multivariate sample selection model with ordinal selection equations. Our DML estimators are doubly-robust and based on the efficient influence functions. Finite sample properties of the proposed estimators are studied and compared on simulated data. Specifically, the results of the analysis suggest that without addressing multivariate sample selection, the estimates of the causal parameters may be highly biased. However, the proposed estimators allow us to avoid these biases.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Frolich, Markus (2007) Nonparametric IV estimation of local average treatment effects with covariates | 0.405 | 1 | 1 | 100% |
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Automatic debiased machine learning and sensitivity analysis for sample selection models | 0.405 | 1 | 1 |