Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities
Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities
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发表时间：
2017-11
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通讯作者：
David A. Hirshberg;Stefan Wager
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作者：
David A. Hirshberg;Stefan Wager
最近，人们对观察性研究中治疗效果估计的双稳健方法产生了浓厚的兴趣，因为人们认识到它们可以与现代机器学习方法相结合，以获得将良好的有限样本性能与渐近效率相结合的估计量。这些方法首先将正则化回归模型拟合到观察到的结果，然后使用残差的加权和对其进行去偏。通常，去偏权重是通过反转仔细调整的倾向得分估计来获得的，并且可以通过渐近论证来证明这种选择的合理性。然而，没有充分的理由相信经过优化调整的倾向模型也会在有限样本中产生经过优化调整的去偏权重。在本文中，我们研究了一种基于使用直接优化最坏情况风险界限的权重的有效治疗效果估计的替代方法；具体来说，这相当于选择权重来均匀平衡已知的一类函数，以高概率捕获结果回归的误差。我们提供了我们的方法达到半参数效率界限的一般条件；特别是，与现有方法不同，我们不假设重叠之外的治疗倾向有任何规律性。在广泛的实验中，我们发现我们的方法（均匀平衡加权）与增强逆倾向加权和目标最大似然估计相比具有优势。
There has been a recent surge of interest in doubly robust approaches to treatment effect estimation in observational studies, driven by a realization that they can be combined with modern machine learning methods to obtain estimators that pair good finite sample performance with asymptotic efficiency. These methods first fit a regularized regression model to the observed outcomes, and then use a weighted sum of residuals to debias it. Typically the debiasing weights are obtained by inverting a carefully tuned estimate of the propensity scores, and this choice can be justified by asymptotic arguments. However, there is no good reason to believe that an optimally tuned propensity model would also yield optimally tuned debiasing weights in finite samples. In this paper, we study an alternative approach to efficient treatment effect estimation based on using weights that directly optimize worst-case risk bounds; concretely, this amounts to selecting weights that uniformly balance out a class of functions known to capture the errors of the outcome regression with high probability. We provide general conditions under which our method achieves the semiparametric efficiency bound; in particular, unlike existing methods, we do not assume any regularity on the treatment propensities beyond overlap. In extensive experiments, we find that our method, weighting for uniform balance, compares favorably to augmented inverse-propensity weighting and targeted maximum likelihood estimation.