Counterfactual Analysis in Dynamic Latent State Models
This work addresses the challenge of counterfactual inference in dynamic latent-state models, which is incremental as it combines existing ideas from causality, state-space models, simulation, and optimization.
The authors tackled the problem of performing counterfactual analysis in dynamic models with hidden states by developing an optimization-based framework that computes upper and lower bounds on counterfactual queries, applying it to a breast cancer case study.
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.