报告时间：2023年5月8日

                上午9:30-10:30

报告地点：中国人民大学明德主楼1016

              （腾讯会议ID：863-772-470）

报告嘉宾：毛小介

报告主题：Identification and Estimation of Long-term Causal Inference via Data Combination

报告摘要

Identification and Estimation of Long-term Causal Inference via Data Combination

In this talk, I will talk about the problem of identifying and estimating the treatment effect of a certain intervention (e.g., a product design or a therapy) on some long-term outcome of interest (e.g., users' long-term satisfaction or patients' long-term health). This problem is very challenging: randomized experiments are often expensive and have short durations, so long-term outcome observations may not be available; observational studies can be cheaper and more likely to collect observations for long-term outcomes, but they are susceptible to confounding bias. In the first part of this talk, I will review some very recent literature that address this challenge by combining experimental and observational data and leveraging their complementary strengths. I will discuss major assumptions in the literature and discuss their strengths and limitations. In the second part of this talk, I will present my latest work on long-term causal inference under a confounding model more general than those in the existing literature.

作者简介

毛小介，清华大学经管学院管理科学与工程系助理教授。2016年获武汉大学数理经济与数理金融学士学位，2021年获得美国康奈尔大学统计与数据科学博士学位。主要研究方向为因果推断、数据驱动的决策理论与方法，研究涉及统计学、 运筹学、机器学习等多个领域。相关研究成果发表于Operations Research、Management Science、Conference on Neural Information Processing Systems (NeurIPS)、International Conference on Machine Learning (ICML)、International Conference on Artificial Intelligence and Statistics (AISTATS)等国际知名学术期刊和学术会议。