Evaluating (weighted) dynamic treatment effects by double machine learning
BP2-STS
- Bodory, Hugo University of St. Gallen, Switzerland
- Huber, Martin ORCID University of Fribourg, Switzerland
- Lafférs, Lukáš Matej Bel University, Slovakia
- 08.06.2022
Published in:
- Econometrics Journal. - 2022, vol. 25, no. 3, p. 628-648
English
We consider evaluating the causal effects of dynamic treatments, i.e., of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate (local) misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high-dimensional covariates. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups, e.g., among those treated in the first treatment period.We demonstrate that the estimators are asymptotically normal and √n-consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study.
- Faculty
- Faculté des sciences économiques et sociales et du management
- Department
- Département d'économie politique
- Language
-
- English
- Classification
- Economics
- License
- License undefined
- Open access status
- green
- Identifiers
- Persistent URL
- https://folia.unifr.ch/unifr/documents/325206
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