Efficient Nonparametric Causal Inference with Missing Exposure Information
Efficient Nonparametric Causal Inference with Missing Exposure Information
复制标题
缺失暴露信息的高效非参数因果推断
DOI：
10.1515/ijb-2019-0087
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Edward H. Kennedy
中科院分区：
文献类型：
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作者：
Edward H. Kennedy
摘要暴露信息缺失是许多观察性研究的一个非常普遍的特征。在这里，我们研究的可识别性和有效的估计向量结果的因果关系的影响，在这种情况下，治疗是无混淆的，但部分失踪。我们考虑随机缺失的情况，治疗中的缺失不仅取决于复杂的协变量，还取决于治疗后的结果。我们给出了一个新的平均治疗效果在这种情况下，沿着与有效的非参数模型中的这个参数的影响函数，这产生了一个非参数的效率界的识别表达式。我们使用后一个结果来构造非参数估计，它们对维数灾难的敏感性比通常的要低，例如。通过具有更快的收敛速度比复杂的滋扰估计，他们依赖。此外，我们表明，这些估计可以根-n一致和渐近正常的弱非参数条件下，即使使用灵活的机器学习。最后，我们将这些结果应用到部分缺失工具变量的因果推理问题。
Abstract Missing exposure information is a very common feature of many observational studies. Here we study identifiability and efficient estimation of causal effects on vector outcomes, in such cases where treatment is unconfounded but partially missing. We consider a missing at random setting where missingness in treatment can depend not only on complex covariates, but also on post-treatment outcomes. We give a new identifying expression for average treatment effects in this setting, along with the efficient influence function for this parameter in a nonparametric model, which yields a nonparametric efficiency bound. We use this latter result to construct nonparametric estimators that are less sensitive to the curse of dimensionality than usual, e. g. by having faster rates of convergence than the complex nuisance estimators they rely on. Further we show that these estimators can be root-n consistent and asymptotically normal under weak nonparametric conditions, even when constructed using flexible machine learning. Finally we apply these results to the problem of causal inference with a partially missing instrumental variable.