Related papers: Counterfactual Analysis in Dynamic Latent State Models

Counterfactual Analysis in Dynamic Latent State Models

URL: http://arxiv.org/abs/2205.13832v4
Date: Fri, 5 May 2023 19:02:26 GMT
Title: Counterfactual Analysis in Dynamic Latent State Models
Authors: Martin Haugh and Raghav Singal
Abstract summary: We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. We are the first to compute lower and upper bounds on a counterfactual query in a dynamic latent-state model.
Score: 2.766648389933265
License: http://creativecommons.org/licenses/by/4.0/
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 prediction'' 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 underlying causal mechanism and the possibility of infinitely 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.

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