报告题目：Double Robust Estimator of Average Causal Treatment Effect for Censored Medical Cost Data
报告人：Xiao-Hua (Andrew) Zhou, 美国华盛顿大学生物统计系教授、“巴渝海外引智计划”入选教授
Abstract: In observational studies, estimation of average causal treatment effect on a patient’s response should adjust for confounders that are associated with both treatment exposure and response. In addition, the response, such as medical cost, may have incomplete follow up. In this article, a double robust estimator is proposed for average causal treatment effect for right censored medical cost data. The estimator is double robust in the sense that it remains consistent when either the model for the treatment assignment or the regression model for the response is correctly specified. Double robust estimators give the analyst more chance to make a valid inference. Asymptotic normality is obtained for the proposed estimator and an estimator for the asymptotic variance is also derived. Simulation studies show good finite sample performance of the proposed estimator and a real data analysis using the proposed method is provided as illustration. This is a joint work with Xuan Wang at Harvard University.