Singh, R;
Xu, L;
Gretton, A;
(2024)
Kernel methods for causal functions: dose, heterogeneous and incremental response curves.
Biometrika
, 111
(2)
pp. 497-516.
10.1093/biomet/asad042.
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Abstract
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous and incremental response curves. The treatment and covariates may be discrete or continuous in general spaces. Due to a decomposition property specific to the reproducing kernel Hilbert space, our estimators have simple closed form solutions. We prove uniform consistency with finite sample rates via an original analysis of generalized kernel ridge regression. We extend our main results to counterfactual distributions and to causal functions identified by front and back door criteria. We achieve state-of-the-art performance in nonlinear simulations with many covariates, and conduct a policy evaluation of the US Job Corps training program for disadvantaged youths.
| Type: | Article |
|---|---|
| Title: | Kernel methods for causal functions: dose, heterogeneous and incremental response curves |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/biomet/asad042 |
| Publisher version: | https://doi.org/10.1093/biomet/asad042 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10174297 |
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