Speaker |
Fabio De Sousa Ribeiro |
|---|---|
Affiliation |
Imperial College London |
Date |
Friday, 09 June 2023 |
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 |
The ability to generate plausible counterfactuals has wide scientific applicability and is particularly valuable in fields like medical imaging, wherein data are scarce and underrepresentation of subgroups is prevalent. Answering counterfactual queries like 'why?' and 'what if..?', expressed in the language of causality, could greatly benefit several important research areas such as: (i) explainability; (ii) data augmentation; (iii) robustness to spurious correlations, and (iv) fairness notions in both observed and counterfactual outcomes. Despite recent progress, accurate estimation of interventional and counterfactual queries for high-dimensional structured variables (e.g. images) remains an open problem. Few previous works have attempted to fulfil all three rungs of Pearl’s ladder of causation, namely: association; intervention and counterfactuals in a principled manner using deep models. Moreover, evaluating counterfactuals poses inherent challenges, as they are by definition counter-to-fact and unobservable. Contrary to preceding studies, which focus primarily on identifiability guarantees in the limit of infinite data, we take a pragmatic approach to counterfactuals. We focus on exploring the practical limits and possibilities of estimating and empirically evaluating high-fidelity image counterfactuals of real-world data. To this end, we introduce a specific system and method which leverages ideas from causal mediation analysis and advances in generative modelling to engineer deep causal mechanisms for structured variables. Our experiments illustrate the ability of our proposed mechanisms to perform accurate abduction and plausible estimates of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals. |
Biography |
Fabio De Sousa Ribeiro is a postdoctoral research associate in the BioMedIA group at Imperial College London working under Professor Ben Glocker. His primary research interests lie at the intersection of causality and deep generative modelling for medical imaging and healthcare applications. His work bolsters the ongoing effort by the machine learning community to combine the central ideas behind causality and deep representation learning to help tackle several challenging research areas such as explainability, robustness and fairness. |