How Causal Inference Connects Statistics and Data Science — Mechanism Investigation for Longitudinal Data

Abstract

Causal inference has become a popular approach for investigating causality and mechanism in complex data format. In the first part, I will provide a general picture of the causal inference framework: defining a scientific question as a causal parameter based on counterfactual model, identifying this causal parameter as a statistical parameter, and then estimating (or testing) this statistical parameter by statistics inference. The mechanism investigation for the effect of religious behavior on survival rate will be used as the motivating example. In the second part, I will show how to define and identify the longitudinal total and mediation effects (two effects of interest in mechanism investigation) as analytic formula, and obtain parametric estimates by Monte-Carlo Simulation and bootstrapping method. I will demonstrate how to extend this framework to address the challenge of undefined mediation effect due to survival censoring.