Evaluating (weighted) dynamic treatment effects by double machine learning

Identification of causal effects using instrumental variables

Hugo Bodory, Martin Huber and Lukáš Lafférs

The Econometrics Journal, 2022, vol. 25, issue 3, 628-648

Abstract: SummaryWe consider evaluating the causal effects of dynamic treatments, i.e., of mul-tiple 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 -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.

Keywords: Dynamic treatment effects; double machine learning; efficient score (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (8)

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Working Paper: Evaluating (weighted) dynamic treatment effects by double machine learning (2021)
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