Hugo Bodory, Martin Huber, Lukáš Lafférs
arXiv 1 Dec 2020 · Econometrics · publishedEconometrics Journal (2022) · 110 citations (OpenAlex)
arXiv:2012.00370 · PDF · DOI · OpenAlex · Extracted main text
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 and is combined with data splitting to prevent overfitting. 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 $\sqrt{n}$-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 in order to assess different sequences of training programs under a large set of covariates.
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 | 15 | 5 | 100% |
| 2 | van der Laan, Polley, and Hubbard (2007) Super Learner | 0.843 | 3 | 3 | 100% |
| 3 | Lechner and Miquel (2010) Identification of the effects of dynamic treatments by sequential conditional independence assumptions | 0.644 | 2 | 2 | 100% |
| 4 | Robins (2000) Marginal Structural Models versus Structural nested Models as Tools for Causal inference | 0.644 | 2 | 2 | 100% |
| 5 | Tran, Yiannoutsos, Wools-Kaloustian, Siika, van der Laan, and Petersen (2019) Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study | 0.644 | 2 | 2 | 100% |
| 6 | Neyman (1959) Optimal asymptotic tests of composite statistical hypotheses | 0.511 | 2 | 1 | 100% |
| 7 | Robins (1986) A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy… | 0.511 | 2 | 1 | 100% |
| 8 | Angrist, Imbens, and Rubin (1996) Identification of Causal Effects using Instrumental Variables | 0.405 | 1 | 1 | 100% |
| 9 | Athey and Imbens (2017) The State of Applied Econometrics: Causality and Policy Evaluation | 0.405 | 1 | 1 | 100% |
| 10 | Athey, Imbens, and Wager (2018) Approximate residual balancing: debiased inference of average treatment effects in high dimensions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.