论文标题
在低依从性设置中的个人治疗处方效应估计
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
论文作者
论文摘要
个人治疗效果（ITE）估计是一个广泛研究的问题，在各个领域中应用。我们对存在随机分配的治疗，健康状况（由于处方不合规）或数字广告（例如竞争和广告阻滞剂）的典型情况（例如竞争和广告阻滞剂）而对存在异质不合规的情况进行建模。依从性越低，治疗处方的效果或单个处方效应（IPE）的效果就越多，信号逐渐消失并难以估计。我们提出了一种估计IPE的新方法，它利用观察到的合规性信息来防止信号褪色。使用结构性因果模型框架和DO-Calculus，我们定义了一个通用的介导的因果效应设置，并提出了相应的估计器，该估计量始终如一地通过渐近方差保证恢复IPE。最后，我们对合成数据集和现实数据集进行了实验，这些实验突出了该方法的好处，该方法始终在低符合性设置中提高最新方法
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health (because of non-compliance to prescription) or digital advertising (because of competition and ad blockers for instance). The lower the compliance, the more the effect of treatment prescription, or individual prescription effect (IPE), signal fades away and becomes hard to estimate. We propose a new approach for the estimation of the IPE that takes advantage of observed compliance information to prevent signal fading. Using the Structural Causal Model framework and do-calculus, we define a general mediated causal effect setting and propose a corresponding estimator which consistently recovers the IPE with asymptotic variance guarantees. Finally, we conduct experiments on both synthetic and real-world datasets that highlight the benefit of the approach, which consistently improves state-of-the-art in low compliance settings
