Counterfactual Explanations in Sequential Decision Making Under Uncertainty

Stratis Tsirtsis, Abir De, Manuel Gomez Rodriguez

Keywords: counterfactual explanations, markov decision process, structural causal model, decision making, dynamic programming, explainability

Abstract: Methods to find counterfactual explanations have predominantly focused on one-step decision making processes. In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time. We start by formally characterizing a sequence of actions and states using finite horizon Markov decision processes and the Gumbel-Max structural causal model. Building upon this characterization, we formally state the problem of finding counterfactual explanations for sequential decision making processes. In our problem formulation, the counterfactual explanation specifies an alternative sequence of actions differing in at most k actions from the observed sequence that could have led the observed process realization to a better outcome. Then, we introduce a polynomial time algorithm based on dynamic programming to build a counterfactual policy that is guaranteed to always provide the optimal counterfactual explanation on every possible realization of the counterfactual environment dynamics. We validate our algorithm using both synthetic and real data from cognitive behavioral therapy and show that the counterfactual explanations our algorithm finds can provide valuable insights to enhance sequential decision making under uncertainty.

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TL;DR: The paper introduces a method to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time

Code: https://github.com/Networks-Learning/counterfactual-explanations-mdp