Conditional average treatment effects estimation is one of the crucial mainstays in observational studies. The conditional average treatment effect is defined as a functional parameter which is used to describe the variation of average treatment effect condition on some covariates. Based on the unconfoundedness assumption, we propose the covariates balance method to estimate the propensity score, and the estimated propensity score is applied to the non-parametric method to estimate the conditional average treatment effect. The proposed method is robust and superior to the parametric approach. The proposed method has a smaller RMSE than the true method when the propensity score model is correct specified. Meanwhile, compared with the kernel method, the proposed method is much more computationally efficient. The proposed estimator is consistent and asymptotic under some regularity conditions. Finally, we apply the proposed method to estimate the effect of maternal smoking on low birth weight infants given the age of mothers.

中文翻译：

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用协变量平衡法估计条件平均治疗效果

条件平均治疗效果估计是观察性研究的重要支柱之一。条件平均治疗效果被定义为一个函数参数，用于描述平均治疗效果条件在某些协变量上的变化。基于无混杂性假设，我们提出了协变量平衡法来估计倾向得分，并将估计的倾向得分应用于非参数方法来估计条件平均治疗效果。所提出的方法是稳健的并且优于参数方法。当倾向得分模型正确指定时，所提出的方法具有比真实方法更小的 RMSE。同时，与核方法相比，该方法的计算效率要高得多。所提出的估计量在某些正则条件下是一致且渐近的。最后，我们应用所提出的方法来估计母亲吸烟对给定母亲年龄的低出生体重婴儿的影响。