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Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves

Rahul Singh, Liyuan Xu, Arthur Gretton

arXiv 10 Oct 2020 · Econometrics · publishedBiometrika (2023) · 6 citations (OpenAlex)

arXiv:2010.04855 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves. Treatment and covariates may be discrete or continuous in general spaces. Due to a decomposition property specific to the RKHS, our estimators have simple closed form solutions. We prove uniform consistency with finite sample rates via 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.

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74
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distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1James Robins (1986) A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy w…0.9285480%
2Whitney K Newey (1994) The asymptotic variance of semiparametric estimators0.8434475%
3Paul R Rosenbaum and Donald B Rubin (1983) The central role of the propensity score in observational studies for causal effects0.8434475%
4Aad van der Vaart (1991) On differentiable functionals0.8434475%
5Xinkun Nie and Stefan Wager (2021) Quasi-oracle estimation of heterogeneous treatment effects0.8115280%
6David A Hirshberg, Arian Maleki, and Jose R Zubizarreta (2019) Minimax linear estimation of the retargeted mean0.7946350%
7Edward H Kennedy, Zongming Ma, Matthew D McHugh, and Dylan S Small (2017) Nonparametric methods for doubly robust estimation of continuous treatment effects0.7375440%
8Andrea Caponnetto and Ernesto De Vito (2007) Optimal rates for the regularized least-squares algorithm0.7374350%
9Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2022) Debiased machine learning of global and local parameters using regularized Riesz representers self0.7373367%
10Nathan Kallus (2020) Generalized optimal matching methods for causal inference0.73732100%

Showing the top 10 of 74 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Sequential kernel embedding for mediated and time-varying dose response curves0.7662011
2Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments0.7301810
3Kernel methods for long term dose response curves0.72186
4Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension0.64442
5A Kernelization-Based Approach to Nonparametric Binary Choice Models0.64422
6Minimax Optimal Kernel Operator Learning via Multilevel Training0.40511
7Stable Probability Weighting Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap0.40511
8=0pt =0pt plus .5=0pt plus .5=.3Identification and Semiparametric Estimation of Conditional Means from Aggregate Data0.40511