Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
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用双重／去偏机器学习有效估计加权累积治疗效应
- 研究问题
- 对于事件发生时间结局，当观察性数据重叠不足且辅助模型容易错设时，如何灵活而有效地估计随限制时间变化的加权累积因果效应。
- 方法与思路
- 作者提出一类一步式交叉拟合双重／去偏机器学习估计量，允许辅助模型使用机器学习，并针对加权累积效应进行理论和模拟分析。
- 材料陈述的贡献
- 摘要称，估计量在正则条件下具有一致性、渐近线性并达到半参数效率界；模拟及英国基层医疗药物比较用于展示方法表现。
- 阅读关注点
- 应核对权重与限制时间如何定义目标效应、删失及重叠要求，以及辅助模型收敛条件、效率界和时间函数推断的具体形式。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未核实；摘要未列出正则与识别条件，加权改变目标后不能直接解释为总体平均治疗效应。
原文摘要
In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled trial data is unavailable. Model misspecification and lack of overlap are common issues in observational studies, and they often lead to inconsistent and inefficient estimators of the average treatment effect. Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification. However, the approaches that allow machine learning for nuisance models have not been extended to the setting of weighted average treatment effects for time-to-event outcomes when there is poor overlap. In this work, we propose a class of one-step cross-fitted double/debiased machine learning estimators for the weighted cumulative causal effect as a function of restriction time. We prove that the proposed estimators are consistent, asymptotically linear, and reach semiparametric efficiency bounds under regularity conditions. Our simulations show that the proposed estimators using nonparametric machine learning nuisance models perform as well as established methods that require correctly-specified parametric nuisance models, illustrating that our estimators mitigate the need for oracle parametric nuisance models. We apply the proposed methods to real-world observational data from a UK primary care database to compare the effects of anti-diabetic drugs on cancer clinical outcomes.