Hello,
The Graduate School of Data Science is pleased to announce the BK21 x ERC Seminar as detailed below. We sincerely invite your interest and participation.
Our speaker, Dr. Junhyung Park, currently a Postdoctoral Researcher at ETH Zürich, will present his research on understanding the relationships between observational and experimental data expressed by causal models through Measure Theory, providing mathematical and probabilistic insights for causal inference.
Date & Time: April 23, 2025, 3:30 PM - 5:00 PM
Location: Seoul National University, Building 942, Room 302
Speaker: Dr. Junhyung Park
Title: Causal Spaces: A Measure Theoretic Axiomatisation of Causality
Abstract:
While the theory of causality is widely viewed as an extension of probability theory, a view which we share, there was no universally accepted, axiomatic framework for causality, analogous to Kolmogorov's measure-theoretic axiomatisation for the theory of probabilities. Instead, many competing frameworks exist, such as the structural causal models or the potential outcomes framework, that mostly have the flavour of statistical models. To fill this gap, we propose the notion of causal spaces, consisting of a probability space along with a collection of transition probability kernels, called causal kernels, which satisfy two simple axioms and which encode causal information that probability spaces cannot encode. The proposed framework is not only rigorously grounded in measure theory, but it also sheds light on long-standing limitations of existing frameworks including, for example, cycles, latent variables and stochastic processes. Our hope is that causal spaces will play the same role for the theory of causality that probability spaces play for the theory of probabilities.
Bio:
Jun is a postdoctoral researcher in the Statistical Machine Learning Group at ETH Zürich, led by Fanny Yang. He received his PhD last year at the Max Planck Institute for Intelligent Systems, Tübingen, under the supervision of Krikamol Muandet and Bernhard Schölkopf. Previously, he obtained his MSc in Statistics at ETH Zürich under Sara van de Geer, and before that, he received his BA and MMath (Part III) degrees at the University of Cambridge. He is interested in the foundational questions of causality, as well as statistical learning theory.