Fetching the paper…
Reading the bibliography…
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves.
Some results on Tchebycheffian spline functions
George Kimeldorf and Grace Wahba · 1971
Earlier work this paper cites.
Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
Peter Craven and Grace Wahba · 1978
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
Earlier work this paper cites.
Asymptotic optimality of CL and generalized cross-validation in ridge regression with application to spline smoothing
Ker-Chau Li · 1986
Earlier work this paper cites.
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
James Robins · 1986
Earlier work this paper cites.
Spline Models for Observational Data
Grace Wahba · 1990
Earlier work this paper cites.
On differentiable functionals
Aad van der Vaart · 1991
Earlier work this paper cites.
Comment: Graphical models, causality and intervention
Judea Pearl · 1993
Earlier work this paper cites.
The asymptotic variance of semiparametric estimators
Whitney K Newey · 1994
Earlier work this paper cites.
Kernel estimation of partial means and a general variance estimator
Whitney K Newey · 1994
Earlier work this paper cites.
Causal diagrams for empirical research
Judea Pearl · 1995
Earlier work this paper cites.
Optimization by Vector Space Methods
David G Luenberger · 1997
Earlier work this paper cites.
On the mathematical foundations of learning
Felipe Cucker and Steve Smale · 2002
Earlier work this paper cites.
Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: Finite sample oracle inequalities and examples
Mark J van der Laan and Sandrine Dudoit · 2003
Earlier work this paper cites.
Causal inference with general treatment regimes: Generalizing the propensity score
Kosuke Imai and David A Van Dyk · 2004
Earlier work this paper cites.
Cross section and panel data estimators for nonseparable models with endogenous regressors
Joseph G Altonji and Rosa L Matzkin · 2005
Earlier work this paper cites.
Predicting the efficacy of future training programs using past experiences at other locations
V Joseph Hotz, Guido W Imbens, and Julie H Mortimer · 2005
Earlier work this paper cites.
On learning vector-valued functions
Charles A Micchelli and Massimiliano Pontil · 2005
Earlier work this paper cites.
A general imputation methodology for nonparametric regression with censored data
Dan Rubin and Mark J van der Laan · 2005
Earlier work this paper cites.
Unifying divergence minimization and statistical inference via convex duality
Yasemin Altun and Alex Smola · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher KI Williams · 2006
Earlier work this paper cites.
Extending marginal structural models through local, penalized, and additive learning
Daniel Rubin and Mark J van der Laan · 2006
Earlier work this paper cites.
Optimal rates for the regularized least-squares algorithm
Andrea Caponnetto and Ernesto De Vito · 2007
Earlier work this paper cites.
Linear inverse problems in structural econometrics estimation based on spectral decomposition and regularization
Marine Carrasco, Jean-Pierre Florens, and Eric Renault · 2007
Earlier work this paper cites.
Efficient semiparametric estimation of quantile treatment effects
Sergio Firpo · 2007
Earlier work this paper cites.
Learning theory estimates via integral operators and their approximations
Steve Smale and Ding-Xuan Zhou · 2007
Cited alongside, same era.
A Hilbert space embedding for distributions
Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf · 2007
Cited alongside, same era.
Does Job Corps work? Impact findings from the national Job Corps study
Peter Z Schochet, John Burghardt, and Sheena McConnell · 2008
Cited alongside, same era.
Support Vector Machines
Ingo Steinwart and Andreas Christmann · 2008
Cited alongside, same era.
Causality
Judea Pearl · 2009
Cited alongside, same era.
Dataset Shift in Machine Learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Neil D Lawrence, and Anton Schwaighofer · 2009
Cited alongside, same era.
Fixing an error in Caponnetto and de Vito (2007)
Danica J Sutherland · 2017
Later among the works it cites.
Minimax estimation of kernel mean embeddings
Ilya Tolstikhin, Bharath K Sriperumbudur, and Krikamol Muandet · 2017
Later among the works it cites.
Policy evaluation and optimization with continuous treatments
Nathan Kallus and Angela Zhou · 2018
Later among the works it cites.
Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes
Loucas Pillaud-Vivien, Alessandro Rudi, and Francis Bach · 2018
Later among the works it cites.
Minimax linear estimation of the retargeted mean
David A Hirshberg, Arian Maleki, and Jose R Zubizarreta · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Max Welling · 2009
Cited alongside, same era.
Efficient semiparametric estimation of multi-valued treatment effects under ignorability
Matias D Cattaneo · 2010
Cited alongside, same era.
On the relation between universality, characteristic kernels and RKHS embedding of measures
Bharath Sriperumbudur, Kenji Fukumizu, and Gert Lanckriet · 2010
Cited alongside, same era.
Reproducing Kernel Hilbert Spaces in Probability and Statistics
Alain Berlinet and Christine Thomas-Agnan · 2011
Cited alongside, same era.
Nonparametric instrumental regression
Serge Darolles, Yanqin Fan, Jean-Pierre Florens, and Eric Renault · 2011
Cited alongside, same era.
On the equivalence between herding and conditional gradient algorithms
Francis Bach, Simon Lacoste-Julien, and Guillaume Obozinski · 2012
Cited alongside, same era.
Kernel instrumental variable regression
Rahul Singh, Maneesh Sahani, and Arthur Gretton · 2019
Later among the works it cites.
Nonparametric estimation of causal heterogeneity under high-dimensional confounding
Michael Zimmert and Michael Lechner · 2019
Later among the works it cites.
Double debiased machine learning nonparametric inference with continuous treatments
Kyle Colangelo and Ying-Ying Lee · 2020
Closest in time.
Sobolev norm learning rates for regularized least-squares algorithms
Simon Fischer and Ingo Steinwart · 2020
Closest in time.
Causal Inference
Miguel A Hernán and James M Robins · 2020
Closest in time.
Direct and indirect effects of continuous treatments based on generalized propensity score weighting
Martin Huber, Yu-Chin Hsu, Ying-Ying Lee, and Layal Lettry · 2020
Closest in time.
Generalized optimal matching methods for causal inference
Nathan Kallus · 2020
Closest in time.
Optimal doubly robust estimation of heterogeneous causal effects
Edward H Kennedy · 2020
Closest in time.
A measure-theoretic approach to kernel conditional mean embeddings
Junhyung Park and Krikamol Muandet · 2020
Closest in time.
Metrizing weak convergence with maximum mean discrepancies
Carl-Johann Simon-Gabriel, Alessandro Barp, and Lester Mackey · 2020
Closest in time.
Kernel methods for policy evaluation: Treatment effects, mediation analysis, and off-policy planning
Rahul Singh, Liyuan Xu, and Arthur Gretton · 2020
Closest in time.
Counterfactual mean embeddings
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam, and Sanparith Marukatat · 2021
Closest in time.
Quasi-oracle estimation of heterogeneous treatment effects
Xinkun Nie and Stefan Wager · 2021
Closest in time.
Debiased machine learning of conditional average treatment effects and other causal functions
Vira Semenova and Victor Chernozhukov · 2021
Closest in time.
Rahul Singh · 2021
Closest in time.
Debiased machine learning of global and local parameters using regularized Riesz representers
Victor Chernozhukov, Whitney K Newey, and Rahul Singh · 2022
Closest in time.
Estimation of conditional average treatment effects with high-dimensional data
Qingliang Fan, Yu-Chin Hsu, Robert P Lieli, and Yichong Zhang · 2022
Closest in time.
Optimal rates for regularized conditional mean embedding learning
Zhu Li, Dimitri Meunier, Mattes Mollenhauer, and Arthur Gretton · 2022
Closest in time.
Sobolev norm learning rates for conditional mean embeddings
Prem Talwai, Ali Shameli, and David Simchi-Levi · 2022
Closest in time.