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SEMIPARAMETRIC BAYESIAN CAUSAL INFERENCE
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DOI：10.1214/19-AOS1919.png)
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摘要
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We develop a semiparametric Bayesian approach for estimating the mean response in a missing data model with binary outcomes and a nonparametrically modelled propensity score. Equivalently, we estimate the causal effect of a treatment, correcting nonparametrically for confounding. We show that standard Gaussian process priors satisfy a semiparametric Bernsteinvon Mises theorem under smoothness conditions. We further propose a novel propensity score-dependent prior that provides efficient inference under strictly weaker conditions. We also show that it is theoretically preferable to model the covariate distribution with a Dirichlet process or Bayesian bootstrap, rather than modelling its density.
Keyword:
Bernstein-von Mises
Gaussian processes
propensity score-dependent priors
causal inference
Dirichlet process
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
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引用论文
ADAPTIVE BAYESIAN ESTIMATION USING A GAUSSIAN RANDOM FIELD WITH INVERSE GAMMA BANDWIDTH.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
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