Michela Bia, Martin Huber, Lukáš Lafférs
arXiv 30 Nov 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 34 citations (OpenAlex)
arXiv:2012.00745 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent under specific regularity conditions concerning the machine learners and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data for evaluating the effect of training on hourly wages which are only observed conditional on employment. The estimator is available in the causalweight package for the statistical software R.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 10 | 3 | 100% |
| 2 | Levy (2019) Tutorial: Deriving The Efficient Influence Curve for Large Models | 0.811 | 4 | 2 | 100% |
| 3 | Huber (2012) Identification of average treatment effects in social experiments under alternative forms of attrition self | 0.737 | 3 | 2 | 100% |
| 4 | Huber (2014) Treatment evaluation in the presence of sample selection self | 0.737 | 3 | 2 | 100% |
| 5 | Neyman (1959) Optimal asymptotic tests of composite statistical hypotheses | 0.737 | 3 | 2 | 100% |
| 6 | Bodory and Huber (2018) The causalweight package for causal inference in R | 0.644 | 2 | 2 | 100% |
| 7 | Das, Newey, and Vella (2003) Nonparametric Estimation of Sample Selection Models | 0.644 | 2 | 2 | 100% |
| 8 | Newey (2007) Nonparametric continuous/discrete choice models | 0.644 | 2 | 2 | 100% |
| 9 | Robins, Rotnitzky, and Zhao (1994) Estimation of Regression Coefficients When Some Regressors Are not Always Observed | 0.644 | 2 | 2 | 100% |
| 10 | Robins, Rotnitzky, and Zhao (1995) Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 63 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.