论文标题
Survcaus：代表生存因果推断
SurvCaus : Representation Balancing for Survival Causal Inference
论文作者
论文摘要
在过去的几年中，个体治疗效果（ITE）估计方法的流行度已上升。在大多数情况下，个体效应更好地表现为条件平均治疗效果（CATE）。最近，从观察数据中，代表性平衡技术在因果推断方面取得了巨大的动力，但仍限于连续（和二进制）结果。但是，在众多病理中，感兴趣的结果是（可能被审查的）生存时间。我们的论文提出了理论保证，用于使用能够在个人层面上审查的情况下，使用能够预测事实和反事实生存函数（然后是CATE）的神经网络，在生存环境中应用于生存环境中的反事实框架的理论保证。我们还提供了有关合成和半合成数据集的广泛实验，这些实验表明所提出的扩展超过了基线方法。
Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years. Most of the time, individual effects are better presented as Conditional Average Treatment Effects (CATE). Recently, representation balancing techniques have gained considerable momentum in causal inference from observational data, still limited to continuous (and binary) outcomes. However, in numerous pathologies, the outcome of interest is a (possibly censored) survival time. Our paper proposes theoretical guarantees for a representation balancing framework applied to counterfactual inference in a survival setting using a neural network capable of predicting the factual and counterfactual survival functions (and then the CATE), in the presence of censorship, at the individual level. We also present extensive experiments on synthetic and semisynthetic datasets that show that the proposed extensions outperform baseline methods.
