Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models
本文用马尔可夫模型和随机微分方程模型说明时间依赖混淆（反馈）和中介是动态概念，强调平均处理效应与处理组处理效应的区别，并指出对离散测量进行中介分析可能误导，需考虑底层连续过程。
Abstract The concept of causality is naturally related to processes developing over time. Central ideas of causal inference like time‐dependent confounding (feedback) and mediation should be viewed as dynamic concepts. We shall study these concepts in the context of simple dynamic systems. Time‐dependent confounding and its implications are illustrated in a Markov model. We emphasize the distinction between average treatment effect, ATE, and treatment effect of the treated, ATT. These effects could be quite different, and we discuss the relationship between them. Mediation is studied in a stochastic differential equation model. A type of natural direct and indirect effects is considered for this model. Mediation analysis of discrete measurements from such processes may give misleading results, and one needs to consider the underlying continuous process. The dynamic and time‐continuous view of causality and mediation is an essential feature, and more attention should be payed to the time aspect in causal inference.