Statistics Seminar (2019-05)

Topic: Causal Inference and Stable Learning

Speaker: Kun Kuang, Tsinghua University

Time: Thursday, Mar 28, 15:00-16:00

Place: Room 217, Guanghua Building 2

Abstract:

Machine learning methods have demonstrated great success in many fields, but most of them are lack of interpretability and stability. Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to make explainable and stable prediction.

In this talk, we will show some new challenges of estimating causal effect in the wild big data scenarios, including (1) high dimensional and noisy variables, and (2) unknown model structure of interactions among variables. To address these challenges, we proposed Differentiated Confounder Balancing (DCB) algorithm. Moreover, by marrying causal inference with machine learning, we proposed a causal regularizer to recover the causations between predictors and response variable, and a stable learning algorithm for stable prediction across unknown testing data.

Introduction:

Kun Kuang is a Ph.D candidate in department of computer science at Tsinghua University. His main research interests include data driven causal analysis, high dimensional inference, and interpretable and stable prediction. He has published several papers in top data mining and machine learning conferences/journals of the relevant field such as SIGKDD, AAAI, and ICDM etc.

Your participation is warmly welcomed!