论文标题
TCFIMT：从单个多个治疗角度来看的时间反事实预测
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
论文作者
论文摘要
确定时间多干预的因果影响有助于决策。受时变偏差，选择偏差和多种干预措施的相互作用的限制，对单个时间数据的多种治疗效应的分离和估计仍然很少见。为了应对这些挑战，我们提出了一个从个人多重治疗角度（TCFIMT）的全面反事实预测的综合框架。 TCFIMT在SEQ2SEQ框架中构建了对抗任务，以减轻选择和时变偏置，并设计基于对比的学习障碍，以将混合治疗效应分离为分离的主要治疗效果和因果相互作用，从而进一步提高了估计精度。通过从不同字段的两个现实世界数据集上实施实验，提出的方法在通过特定治疗方法预测未来的结果以及选择最佳治疗类型和时机时表现出令人满意的性能。
Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
