论文标题
基于高度自适应拉索的非参数反概率加权估计器
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
论文作者
论文摘要
反概率加权估计量是估计因果效应的最古老，可能是最常用的一类程序。通过通过加权机制调整选择偏见，这些程序通过构建伪群来估计感兴趣的影响，从而消除了选择偏见。尽管它们易于使用，但这些估计量仍需要正确规范加权机制的模型，但已知效率低下，并且遭受了维数的诅咒。我们提出了一类非参数逆概率加权估计器，其中通过平滑地介绍了高度适应性套索，该估计是一种非参数回归函数，证明是一种非参数回归函数，可以在$ n^{ - 1/3} $中收敛 - 对真实的加权机制进行评分。我们证明我们的估计器是渐近线性的，方差会融合到非参数效率结合。与双重稳健的估计器不同，我们的过程既不需要有效影响函数的衍生，也不需要条件结果模型的规范。我们的理论发展对大型统计模型和各种问题设置的有效反概率加权估计量的构建具有广泛的影响。我们在模拟研究中评估了估计量的实际性能，并通过大规模流行病学研究的数据证明了我们提出的方法的使用。
Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biases via a weighting mechanism, these procedures estimate an effect of interest by constructing a pseudo-population in which selection biases are eliminated. Despite their ease of use, these estimators require the correct specification of a model for the weighting mechanism, are known to be inefficient, and suffer from the curse of dimensionality. We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at $n^{-1/3}$-rate to the true weighting mechanism. We demonstrate that our estimators are asymptotically linear with variance converging to the nonparametric efficiency bound. Unlike doubly robust estimators, our procedures require neither derivation of the efficient influence function nor specification of the conditional outcome model. Our theoretical developments have broad implications for the construction of efficient inverse probability weighted estimators in large statistical models and a variety of problem settings. We assess the practical performance of our estimators in simulation studies and demonstrate use of our proposed methodology with data from a large-scale epidemiologic study.
