Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making

We address the challenge of explaining counterfactual outcomes in multi-agent Markov decision processes. In particular, we aim to explain the total counterfactual effect of an agent's action to some realized outcome through its influence on the environment dynamics and the agents' behavior. To achieve this, we introduce a novel causal explanation formula that decomposes the counterfactual effect of an agent's action by attributing to each agent and state variable a score reflecting its respective contribution to the effect.