Events

Prof. Martin Haugh
Associate Professor of Analytics and Operations Research
Department of Analytics, Marketing & Operations
Imperial College Business School

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 the space of tehse mechanisms 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 to the well-known “cheating at the casino” hidden Markov model as well as a case study on breast cancer. 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. (Based on joint work with Raghav Singal.)