NEW G-FORMULA FOR THE SEQUENTIAL CAUSAL EFFECT AND BLIP EFFECT OF TREATMENT IN SEQUENTIAL CAUSAL INFERENCE
成果类型：
Article
署名作者：
Wang, Xiaoqin; Yin, Li
署名单位：
University of Gavle; Karolinska Institutet
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/18-AOS1795
发表日期：
2020
页码：
138-160
关键词：
摘要：
In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatment on the potential outcome after the last treatment. The well-known G-formula expresses these causal effects in terms of the standard parameters. In this article, we obtain a new G-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new G-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.