arXiv 17 Feb 2020 · Econometrics · 8 citations (OpenAlex)
arXiv:2002.07285 · PDF · DOI · OpenAlex · Extracted main text
We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes or the state of the treated unit. We propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments, which can be viewed as a Neyman orthogonal (locally robust) cross-fitted version of $g$-estimation in the dynamic treatment regime. Our method applies to a general class of non-linear dynamic treatment models known as Structural Nested Mean Models and allows the use of machine learning methods to control for potentially high dimensional state variables, subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the structural parameters of interest. These structural parameters can be used for off-policy evaluation of any target dynamic policy at parametric rates, subject to semi-parametric restrictions on the data generating process. Our work is based on a recursive peeling process, typical in $g$-estimation, and formulates a strongly convex objective at each stage, which allows us to extend the $g$-estimation framework in multiple directions: i) to provide finite sample guarantees, ii) to estimate non-linear effect heterogeneity with respect to fixed unit characteristics, within arbitrary function spaces, enabling a dynamic analogue of the RLearner algorithm for heterogeneous effects, iii) to allow for high-dimensional sparse parameterizations of the target structural functions, enabling automated model selection via a recursive lasso algorithm. We also provide guarantees for data stemming from a single treated unit over a long horizon and under stationarity conditions.
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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 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 10 | 5 | 80% |
| 2 | James M. Robins (2004) Optimal Structural Nested Models for Optimal Sequential Decisions, pages 189–326 | 0.874 | 8 | 2 | 100% |
| 3 | Miguel A Hernán and James M Robins (2010) Causal inference: What if, 2010 | 0.811 | 4 | 2 | 100% |
| 4 | Dylan J Foster and Vasilis Syrgkanis (2019) Orthogonal statistical learning self | 0.737 | 3 | 3 | 67% |
| 5 | Bibhas Chakraborty and Erica E. M. Moodie (2013) Semi-parametric Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes, pages 53–78 | 0.737 | 3 | 2 | 100% |
| 6 | James M Robins (1994) Correcting for non-compliance in randomized trials using structural nested mean models | 0.737 | 3 | 2 | 100% |
| 7 | Peter M Robinson (1988) Root-n-consistent semiparametric regression | 0.737 | 3 | 2 | 100% |
| 8 | Stijn Vansteelandt, Marshall Joffe, et al (2014) Structural nested models and g-estimation: the partially realized promise | 0.737 | 3 | 2 | 100% |
| 9 | Chunrong Ai and Xiaohong Chen (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.644 | 2 | 2 | 100% |
| 10 | Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura, Whit… Locally Robust Semiparametric Estimation | 0.644 | 2 | 2 | 100% |
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