Missing attributes are ubiquitous in causal inference, as they are in most applied statistical work. In this paper we consider various sets of assumptions under which causal inference is possible despite missing attributes and discuss corresponding approaches to average treatment effect estimation, including generalized propensity score methods and multiple imputation. Across an extensive simulation study, we show that no single method systematically outperforms others. We find, however, that doubly robust modifications of standard methods for average treatment effect estimation with missing data repeatedly perform better than their nondoubly robust baselines; for example, doubly robust generalized propensity score methods beat inverse-weighting with the generalized propensity score. This finding is reinforced in an analysis of an observational study on the effect on mortality of tranexamic acid administration among patients with traumatic brain injury in the context of critical care management. Here, doubly robust estimators recover confidence intervals that are consistent with evidence from randomized trials, whereas nondoubly robust estimators do not.

中文翻译：

---

属性缺失的双稳健治疗效果估计

正如在大多数应用的统计工作中一样，因果推论中缺少属性。在本文中，我们考虑了各种假设，尽管缺少属性，但仍可以进行因果推理，并讨论了平均治疗效果估计的相应方法，包括广义倾向评分方法和多重推算。在广泛的仿真研究中，我们显示没有任何一种方法能够系统地胜过其他方法。然而，我们发现，在缺少数据的情况下，用于平均治疗效果评估的标准方法的双重鲁棒性修改比其非双重鲁棒性基线表现更好。例如，具有双重健壮性的广义倾向性得分方法与广义倾向性得分相比反加权。在一项对重症监护管理背景下脑外伤患者服用氨甲环酸的死亡率影响的观察性研究的分析中，这一发现得到了加强。在这里，双稳健估计量恢复的置信区间与来自随机试验的证据一致，而非双稳健估计量则没有。