Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
arXiv 25 Mar 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2203.13887 · PDF · DOI · OpenAlex · Extracted main text
We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals. We show that the multiply robust formula for the dynamic treatment regime with discrete treatments can be re-stated in terms of a recursive Riesz representer characterization of nested mean regressions. We then apply a recursive Riesz representer estimation learning algorithm that estimates de-biasing corrections without the need to characterize how the correction terms look like, such as for instance, products of inverse probability weighting terms, as is done in prior work on doubly robust estimation in the dynamic regime. Our approach defines a sequence of loss minimization problems, whose minimizers are the mulitpliers of the de-biasing correction, hence circumventing the need for solving auxiliary propensity models and directly optimizing for the mean squared error of the target de-biasing correction. We provide further applications of our approach to estimation of dynamic discrete choice models and estimation of long-term effects with surrogates.
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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 | Heejung Bang and James M Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.928 | 4 | 3 | 100% |
| 2 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2021) Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression self | 0.737 | 3 | 2 | 100% |
| 3 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters self | 0.644 | 4 | 1 | 100% |
| 4 | Richard D Gill and James M Robins (2001) Causal inference for complex longitudinal data: The continuous case | 0.644 | 2 | 2 | 100% |
| 5 | Miguel A Hernán, Babette Brumback, and James M Robins (2001) Marginal structural models to estimate the joint causal effect of nonrandomized treatments | 0.644 | 2 | 2 | 100% |
| 6 | Liliana Orellana, Andrea Rotnitzky, and James M Robins (2010) Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part i: main content | 0.644 | 2 | 2 | 100% |
| 7 | James Robins (1986) A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy w… | 0.644 | 2 | 2 | 100% |
| 8 | James M Robins (2000) Marginal structural models versus structural nested models as tools for causal inference | 0.644 | 2 | 2 | 100% |
| 9 | Mark J van der Laan, Maya L Petersen, and Marshall M Joffe (2005) History-adjusted marginal structural models and statically-optimal dynamic treatment regimens | 0.644 | 2 | 2 | 100% |
| 10 | Stijn Vansteelandt and Els Goetghebeur (2003) Causal inference with generalized structural mean models | 0.644 | 2 | 2 | 100% |
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