Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences. We propose a new orthogonal score method for treatment effect estimation and inference that results in asymptotically valid confidence intervals assuming only good estimation properties of the hazard outcome model and the conditional probability of treatment. This guarantee allows us to provide valid inference for the conditional treatment effect under the high-dimensional additive hazards model under considerably more generality than existing approaches. In addition, we develop a new Hazards Difference (HDi), estimator. We showcase that our approach has double-robustness properties in high dimensions: with cross-fitting, the HDi estimate is consistent under a wide variety of treatment assignment models; the HDi estimate is also consistent when the hazards model is misspecified and instead the true data generating mechanism follows a partially linear additive hazards model. We further develop a novel sparsity doubly robust result, where either the outcome or the treatment model can be a fully dense high-dimensional model. We apply our methods to study the treatment effect of radical prostatectomy versus conservative management for prostate cancer patients using the SEER-Medicare Linked Data.

翻译：在高维环境下估计生存结果的因果效应，对于许多生物医学应用及社会科学领域而言，都是一个极其重要的课题。我们提出了一种新的正交评分方法，用于治疗效应的估计与推断，该方法仅需风险结果模型和治疗条件概率具备良好的估计性质，即可得到渐近有效的置信区间。这一保证使我们能够在比现有方法更一般的条件下，为高维加性风险模型下的条件治疗效应提供有效推断。此外，我们开发了一种新的风险差（HDi）估计量。我们证明，我们的方法在高维情形下具有双重稳健性：通过交叉拟合，HDi估计量在多种治疗分配模型下均具有一致性；当风险模型被错误设定，而真实数据生成机制遵循部分线性加性风险模型时，HDi估计量同样具有一致性。我们进一步提出了一种新颖的稀疏性双重稳健结果，即结果模型或治疗模型中可以有一个是完全稠密的高维模型。我们将所提方法应用于SEER-Medicare关联数据，以研究根治性前列腺切除术与保守治疗对前列腺癌患者的治疗效果。