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Counterfactual analysis in dynamic latent-state models
Title: | Counterfactual analysis in dynamic latent-state models |
Authors: | Haugh, M Singal, R |
Item Type: | Conference Paper |
Abstract: | We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. Our framework is grounded in the “abduction, action, and predic- tion” approach to answer counterfactual queries and handles two key challenges where (1) the states are hidden and (2) the model is dynamic. Recognizing the lack of knowledge on the under- lying causal mechanism and the possibility of in- finitely many such mechanisms, we optimize over this space and compute upper and lower bounds on the counterfactual quantity of interest. Our work brings together ideas from causality, state- space models, simulation, and optimization, and we apply it on a breast cancer case study. To the best of our knowledge, we are the first to compute lower and upper bounds on a counterfactual query in a dynamic latent-state model. |
Issue Date: | 23-Jul-2023 |
Date of Acceptance: | 24-Apr-2023 |
URI: | http://hdl.handle.net/10044/1/109047 |
Publisher: | MLResearchPress |
Start Page: | 12647 |
End Page: | 12677 |
Journal / Book Title: | Proceedings of Machine Learning Research |
Volume: | 202 |
Copyright Statement: | © Copyright 2023 by the author(s). |
Conference Name: | International Conference on Machine Learning 2023 |
Publication Status: | Published |
Start Date: | 2023-07-23 |
Finish Date: | 2023-07-29 |
Conference Place: | Hawaii, USA |
Appears in Collections: | Imperial College Business School |