Inverse probability of treatment weighting (IPTW), which has been used to estimate average treatment effects (ATE) using observational data, tenuously relies on the positivity assumption and the correct specification of the treatment assignment model, both of which are problematic assumptions in many observational studies. Various methods have been proposed to overcome these challenges, including truncation, covariate‐balancing propensity scores, and stable balancing weights. Motivated by an observational study in spine surgery, in which positivity is violated and the true treatment assignment model is unknown, we present the use of optimal balancing by kernel optimal matching (KOM) to estimate ATE. By uniformly controlling the conditional mean squared error of a weighted estimator over a class of models, KOM simultaneously mitigates issues of possible misspecification of the treatment assignment model and is able to handle practical violations of the positivity assumption, as shown in our simulation study. Using data from a clinical registry, we apply KOM to compare two spine surgical interventions and demonstrate how the result matches the conclusions of clinical trials that IPTW estimates spuriously refute.

中文翻译：

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在脊柱外科手术观察研究中，使用核仁最优匹配更可靠地估计平均治疗效果

治疗加权的逆概率（IPTW）已用于使用观察数据来估计平均治疗效果（ATE），一直依赖于阳性假设和治疗分配模型的正确规范，这在许多观察中都是有问题的假设学习。已经提出了各种方法来克服这些挑战，包括截断，协变量平衡倾向得分和稳定的平衡权重。出于脊柱外科手术中一项观察性研究的动机，在这种研究中，积极性受到侵犯，真正的治疗分配模型尚不清楚，我们提出了通过内核最优匹配（KOM）进行最优平衡估计ATE的方法。通过统一控制一类模型的加权估计器的条件均方误差，如我们的模拟研究所示，KOM同时缓解了治疗分配模型可能出现的错误指定问题，并且能够处理实际的对阳性假设的违反。使用来自临床注册中心的数据，我们将KOM应用于两种脊柱外科手术干预措施的比较，并证明结果与IPTW虚假驳斥的临床试验结论相符。