Semiparametric Bayesian causal inference

DOI10.1214/19-AOS1919zbMath1471.62348arXiv1808.04246OpenAlexW3104714948MaRDI QIDQ2215769

Kolyan Ray, Aad W. van der Vaart

Publication date: 14 December 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.04246

zbMATH Keywords

Gaussian processesDirichlet processcausal inferenceBernstein-von Mises processpropensity score-dependent priors

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Asymptotic properties of nonparametric inference (62G20) Nonparametric tolerance and confidence regions (62G15)

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Simulation‐based estimators of analytically intractable causal effects, Estimating the effects of a California gun control program with multitask Gaussian processes, Causal inference under mis-specification: adjustment based on the propensity score (with discussion), On the Bernstein-von Mises theorem for the Dirichlet process

Cites Work

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• The Bayesian analysis of complex, high-dimensional models: can it be CODA?
• Higher order tangent spaces and influence functions
• Rates of contraction for posterior distributions in \(L^{r}\)-metrics, \(1 \leq r \leq \infty\)
• The semiparametric Bernstein-von Mises theorem
• Information and asymptotic efficiency in parametric-nonparametric models
• A semiparametric Bernstein-von Mises theorem for Gaussian process priors
• Bernstein-von Mises theorem for linear functionals of the density
• On differentiable functionals
• Adaptive Bayesian estimation using a Gaussian random field with inverse gamma bandwidth
• A Bernstein-von Mises theorem for smooth functionals in semiparametric models
• Rates of contraction of posterior distributions based on Gaussian process priors
• Bayesian inference for causal effects: The role of randomization
• Exchangeably weighted bootstraps of the general empirical process
• On the Bernstein-von Mises theorem with infinite-dimensional parameters
• Minimax estimation of a functional on a structured high-dimensional model
• Regularization and confounding in linear regression for treatment effect estimation
• Introduction to double robust methods for incomplete data
• Prior distributions on spaces of probability measures
• Weak convergence and empirical processes. With applications to statistics
• Lower bounds for posterior rates with Gaussian process priors
• Semiparametric minimax rates
• Bayesian inference with rescaled Gaussian process priors
• Recursive partitioning for heterogeneous causal effects
• Mathematical Foundations of Infinite-Dimensional Statistical Models
• Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh
• Higher order influence functions and minimax estimation of nonlinear functionals
• The central role of the propensity score in observational studies for causal effects
• Asymptotic Statistics
• On Profile Likelihood
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• Semiparametric Efficiency in Multivariate Regression Models with Missing Data
• Treatment Effects: A Bayesian Perspective
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Fundamentals of Nonparametric Bayesian Inference