Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
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| Format: | Preprint |
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2021
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| _version_ | 1866915198664704000 |
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| author | Singh, Rahul Xu, Liyuan Gretton, Arthur |
| author_facet | Singh, Rahul Xu, Liyuan Gretton, Arthur |
| contents | We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression. By embedding Pearl's mediation formula and Robins' g-formula with kernels, we allow treatments, mediators, and covariates to be continuous in general spaces, and also allow for nonlinear treatment-confounder feedback. Our key innovation is a reproducing kernel Hilbert space technique called sequential kernel embedding, which we use to construct simple estimators that account for complex feedback. Our estimators preserve the generality of classic identification while also achieving nonasymptotic uniform rates. In nonlinear simulations with many covariates, we demonstrate strong performance. We estimate mediated and time-varying dose response curves of the US Job Corps, and clean data that may serve as a benchmark in future work. We extend our results to mediated and time-varying treatment effects and counterfactual distributions, verifying semiparametric efficiency and weak convergence. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2111_03950 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves Singh, Rahul Xu, Liyuan Gretton, Arthur Methodology Machine Learning Econometrics We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression. By embedding Pearl's mediation formula and Robins' g-formula with kernels, we allow treatments, mediators, and covariates to be continuous in general spaces, and also allow for nonlinear treatment-confounder feedback. Our key innovation is a reproducing kernel Hilbert space technique called sequential kernel embedding, which we use to construct simple estimators that account for complex feedback. Our estimators preserve the generality of classic identification while also achieving nonasymptotic uniform rates. In nonlinear simulations with many covariates, we demonstrate strong performance. We estimate mediated and time-varying dose response curves of the US Job Corps, and clean data that may serve as a benchmark in future work. We extend our results to mediated and time-varying treatment effects and counterfactual distributions, verifying semiparametric efficiency and weak convergence. |
| title | Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves |
| topic | Methodology Machine Learning Econometrics |
| url | https://arxiv.org/abs/2111.03950 |