SEMIPARAMETRIC BAYESIAN CAUSAL INFERENCE
SEMIPARAMETRIC BAYESIAN CAUSAL INFERENCE
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DOI：
10.1214/19-aos1919
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发表时间：
2020-10-01
影响因子：
4.5
通讯作者：
van der Vaart, Aad
中科院分区：
文献类型：
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作者：
Ray, Kolyan;van der Vaart, Aad
我们开发了一个半参数贝叶斯方法估计的平均响应在一个缺失的数据模型与二进制的结果和非参数建模的倾向得分。同样，我们估计治疗的因果效应，对混杂进行非参数校正。我们证明了标准高斯过程先验在光滑条件下满足半参数Bernsteinvon Mises定理。我们进一步提出了一种新的倾向分数相关的先验知识，在严格较弱的条件下提供有效的推理。我们还表明，它是理论上最好的模型协变量分布与狄利克雷过程或贝叶斯自助，而不是建模其密度。
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.