Speaker |
Siyuan Guo |
|---|---|
Affiliation |
University of Cambridge & Max Planck Institute for Intelligent Systems |
Date |
Friday, 04 November 2022 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Learning causal structure from observational data often assumes we observe independent and identically distributed (i.i.d.) data. It aims to find a graphical representation that encodes the same set of conditional independence relationships as those present in the observed distribution. It is known that even with unlimited data, there is a limit to how fine-grained a causal structure we can identify. To overcome this limitation of the i.i.d. setting, recent work has explored using data originating from different, related environments to learn richer causal structures. These approaches implicitly rely on the independent causal mechanisms (ICM) principle, which postulates that the mechanism giving rise to an effect given its causes and the mechanism which generates the causes do not inform or influence each other. Thus, components of the causal model can independently change from environment to environment. Despite its wide application in machine learning and causal inference, there is a lack of statistical formalization of the ICM principle and how it enables the identification of richer causal structures from grouped data. Here we present new Causal de Finetti theorems which offer the first statistical formalization of the ICM principle and show how causal structure identification is possible from exchangeable data. |
Biography |
Siyuan is a PhD student with Ferenc Huszár at the University of Cambridge and Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems. She is a fellow under the Cambridge-Tübingen fellowship and funded by Premium Research Studentship. Her research interest lies in the intersection of causal inference and machine learning. She is interested in developing both theoretical frameworks and methodologies to enable the transfer of ML algorithms from traditional i.i.d. regimes to non-i.i.d tasks. Previously, she studied Machine Learning (MSc) at UCL and Mathematics (BA + MMath) at Cambridge. |