In:
Biometrika, Oxford University Press (OUP), ( 2023-07-05)
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. Because of 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 programme for disadvantaged youths.
Type of Medium:
Online Resource
ISSN:
0006-3444
,
1464-3510
DOI:
10.1093/biomet/asad042
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2023
detail.hit.zdb_id:
1119-8
detail.hit.zdb_id:
1470319-1
SSG:
12
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