论文标题
DRTCI：暂时性因果推断的学习分解表示
DRTCI: Learning Disentangled Representations for Temporal Causal Inference
论文作者
论文摘要
评估患者替代治疗计划的医疗专业人员经常会遇到时间变化的混杂因素，或者会影响未来治疗任务和患者结果的协变量。最近提出的反事实反复网络（CRN）通过使用对抗性训练来平衡患者数据的经常性历史表示来说明时间变化的混杂因素。但是，这项工作假定所有时间变化的协变量都令人困惑，因此试图平衡整个状态代表。鉴于实际上可能是混淆的协变量的实际子集是一般未知的，在静态，非时空环境中反事实评估的最新工作表明，将协变量的表示分解为单独的因素，在这些因素中，每种因素都会影响治疗选择，或者两者都可以使患者的结果或两者都可以限制选择偏见和限制平衡的努力，而不必将其造成均无因素的影响，从而允许造成不存在的因素，从而造成了持续不断的影响。
Medical professionals evaluating alternative treatment plans for a patient often encounter time varying confounders, or covariates that affect both the future treatment assignment and the patient outcome. The recently proposed Counterfactual Recurrent Network (CRN) accounts for time varying confounders by using adversarial training to balance recurrent historical representations of patient data. However, this work assumes that all time varying covariates are confounding and thus attempts to balance the full state representation. Given that the actual subset of covariates that may in fact be confounding is in general unknown, recent work on counterfactual evaluation in the static, non-temporal setting has suggested that disentangling the covariate representation into separate factors, where each either influence treatment selection, patient outcome or both can help isolate selection bias and restrict balancing efforts to factors that influence outcome, allowing the remaining factors which predict treatment without needlessly being balanced.
