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 on the outcome of a realized scenario 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 by attributing to each agent and state variable a score reflecting their respective contributions to the effect. First, we show that the total counterfactual effect of an agent's action can be decomposed into two components: one measuring the effect that propagates through all subsequent agents' actions and another related to the effect that propagates through the state transitions. Building on recent advancements in causal contribution analysis, we further decompose these two effects as follows. For the former, we consider agent-specific effects -- a causal concept that quantifies the counterfactual effect of an agent's action that propagates through a subset of agents. Based on this notion, we use Shapley value to attribute the effect to individual agents. For the latter, we consider the concept of structure-preserving interventions and attribute the effect to state variables based on their "intrinsic" contributions. Through extensive experimentation, we demonstrate the interpretability of our decomposition approach in a Gridworld environment with LLM-assisted agents and a sepsis management simulator.

本研究解决了在多智能体马尔可夫决策过程中解释反事实结果的挑战。通过引入一种新颖的因果解释公式，我们能够将代理行动对结果的反事实效应分解为各代理和状态变量对该效应的贡献分数，进而展示出该方法在理解和解释多智能体互动过程中的潜在影响。

多智能体序列决策中的反事实效应分解