Identification and Estimation of Long-term Causal Inference via Data Combination
Holder: Xiaojie Mao (Tsinghua University)
Time:2024-03-07 15:10-17:00
Location:Room 217, Guanghua Building 2
Abstract:
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 gold-standard for causal inference but they 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 recent literature that addresses 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.
About the Speaker:
毛小介,清华大学经济管理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系学士学位,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。相关研究成果发表于Management Science、Operations Research、Journal of Machine Learning Research、NeurIPS、ICML、AISTATS、COLT等国际知名学术期刊和学术会议。现主持国家自然科学基金优秀青年项目和青年项目,参与国家自然科学基金重大项目和科技部科技创新2030-重大项目等。
Your participation is warmly welcomed!
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