Establishing causal relationships between actions and outcomes is fundamental for accountable multi-agent decision-making. However, interpreting and quantifying agents' contributions to such relationships pose significant challenges. These challenges are particularly prominent in the context of multi-agent sequential decision-making, where the causal effect of an agent's action on the outcome depends on how other agents respond to that action. In this paper, our objective is to present a systematic approach for attributing the causal effects of agents' actions to the influence they exert on other agents. Focusing on multi-agent Markov decision processes, we introduce agent-specific effects (ASE), a novel causal quantity that measures the effect of an agent's action on the outcome that propagates through other agents. We then turn to the counterfactual counterpart of ASE (cf-ASE), provide a sufficient set of conditions for identifying cf-ASE, and propose a practical sampling-based algorithm for estimating it. Finally, we experimentally evaluate the utility of cf-ASE through a simulation-based testbed, which includes a sepsis management environment.

翻译：建立行动与结果之间的因果关系是实现可问责多智能体决策的基础。然而，解释和量化智能体对这些关系的贡献构成了重大挑战。这些挑战在多智能体序贯决策场景中尤为突出——智能体行动对结果的因果效应取决于其他智能体如何响应这一行动。本文旨在提出一种系统化方法，将智能体行动的因果效应归因于其对其他智能体施加的影响。聚焦多智能体马尔可夫决策过程，我们引入智能体特定效应（ASE）这一新型因果量，用于度量智能体行动通过其他智能体传播后对结果产生的影响。随后转向ASE的反事实对应量（cf-ASE），给出识别cf-ASE的充分条件集，并提出一种实用的基于采样的估计算法。最后，通过包含脓毒症管理环境的仿真测试平台，实验评估了cf-ASE的实用价值。