Seminar by Dr. Kwangho Kim

【Date&Time】
5 August,2024 (Monday) 16:00-17:00
Admission Free, No Booking Necessary
【Place】
ISM (room D313-314)
Online: https://us06web.zoom.us/j/87118916638
【Speaker】
Dr. Kwangho Kim
【Title】
Causal and Counterfactual Learning
【Abstract】
Causal inference is all about learning counterfactual parameters, i.e., about what would happen to some response when a “cause” of interest is changed or intervened upon. Many problems in modern causal inference often involve non-trivial structures including time-varying confounding, non-overlapping covariate distributions, high degree of heterogeneity, often with very large sets. These inherent complexities are not properly addressed in common approaches for causal Inference. In the first part of my talk, I will give a brief overview of my recent research to overcome some of these methodological limitations, aiming to bridge the gap between classical causal inference and modern machine learning. Tools in causal inference have also recently shown promise for enhancing machine learning models by allowing them to efficiently generalize to unseen data and unforeseen situations. In the second part of my talk, I introduce a novel notion of counterfactual prediction as a new tool for decision-making under hypothetical (contrary to fact) scenarios.
【Bio】
Kwangho Kim is an assistant professor in the department of statistics at Korea University. Before joining Korea University, he was a Marshall J. Seidman Fellow in the Department of Health Care Policy at Harvard Medical School. He received a Ph.D. in Statistics and Machine Learning at Carnegie Mellon University and an M.S. in Statistics from Stanford University. Prior to that, he earned a B.S. in mathematics and a B.S. in electrical engineering from KAIST in Korea. Dr. Kim’s research spans causal inference, statistical machine learning, non- and semi-parametric statistics, and topological data analysis. Dr. Kim has received multiple awards, including the ASA, IMS, and WNAR graduate student awards.