Partial Counterfactual Identification from Observational and Experimental Data
发布时间:2022-06-15        浏览次数:245

报告题目:Partial Counterfactual Identification from Observational and Experimental Data

报告人:田进(Iowa State University, Associate Professor 

主持人:孙仕亮  教授、石东昱  博士

报告时间:2022年6月17日星期五 上午09:00-10:30

报告地点:腾讯会议(会议号:655-455-752/ 密码:2633


This lecture introduces the progress in the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram. We show that all counterfactual distributions in an arbitrary structural causal model (SCM) could be generated by a canonical family of SCMs with the same causal diagram where unobserved (exogenous) variables are discrete with a finite domain. Utilizing the canonical SCMs, we translate the problem of bounding counterfactuals into that of polynomial programming whose solution provides optimal bounds for the counterfactual query. Solving such polynomial programs is in general computationally expensive. We therefore develop effective Monte Carlo algorithms to approximate optimal bounds from a combination of observational and experimental data. Our algorithms are validated extensively on synthetic and real-world datasets.


       田进博士,1992年本科毕业于清华大学物理系,1997年硕士毕业于加州大学洛杉矶分校物理系,2002年博士毕业于加州大学洛杉矶分校计算机科学系,师从图灵奖得主、贝叶斯网络创始人、现代因果推理/反事实推理创始人Judea Pearl教授,其后在爱荷华州立大学计算机科学系任教,为现代因果推理及概率图模型的发展做出了多项基础性贡献,多年来持续在包括ICML、NeurIPS、AAAI、UAI、JMLR等会议、杂志上发表学术论文,获得包括outstanding paper在内的多项荣誉。田进博士的研究兴趣主要集中在因果推理、概率图模型等机器学习基础领域。

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