DMIR实验室

2018年12月31日上午，卡内基梅隆大学张坤博士应蔡瑞初教授邀请到访DMIR实验室，为实验室师生作了因果关系最新研究进展的相关报告。

报告开始，张坤老师以Simpson’s Paradox中的一个简单例子作为Motivating Example，道出：在生活中，如果仅仅关注事件的整体，而忽略了局部的因果信息，我们很容易做出错误的决策，从而引出了因果最本质、最初始的三个问题：Prediction, Intervention And Counterfactual. 从用传统的方法寻找因果信息，到数据中的因果发现问题，再到Artificial “Intelligence”是否真的“Intelligence”，张坤老师用了许多简单的例子和图表，从哲学的角度上与我们说明Causal Thinking Makes a Difference和Knowing Effects May Be More Informative,进而向我们阐述了因果研究的重要性与必要性。而后，他分别从Preliminaries, Identification of Causal Effects, Causal Discovery(Conditional Independence-based Methods,  Linear Non-Gaussian Methods, Nonlinear Methods and Practical Issues) and Causality-Based Machine Learning等角度为我们简要而有力地介绍了因果不同研究方向的目前研究进展与难点，其中Practical Issues包括了Nonstationarity/heterogeneity, Measurement error, Selection bias, Missing values, Causality in time series等问题。

DMIR实验室师生参与了此次报告，就报告的相关研究内容与张坤老师展开了热烈的学术交流，现场互动频繁，气氛热烈。

张坤博士简介：

Kun Zhang is an assistant professor in the philosophy department at Carnegie Mellon University (CMU). Before joining CMU, he was a senior research scientist at Max-Planck Institute for Intelligent Systems, Germany, and a lead scientist at Information Sciences Institute, University of Southern California. He obtained his Ph.D from Chinese University of Hong Kong and then worked at University of Helsinki as a postdoctoral fellow. His main research interests include causal discovery, machine learning, and large-scale data analysis, and has organized various academic activities to foster interdisciplinary research in causality.