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 the 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负责的多智能体决策的基础。然而，解释和量化智能体对这种关系的贡献带来了重大挑战。这些挑战在多智能体序列决策中尤为突出，因为一个智能体的行动对结果的因果效应取决于其他智能体对该行动的响应。在本文中，我们的目标是提出一种系统方法，将智能体行动的因果效应归因于它们对其他智能体施加的影响。聚焦于多智能体马尔可夫决策过程，我们引入了特定智能体效应（ASE），这是一种新颖的因果量，用于衡量一个智能体的行动通过其他智能体传播对结果产生的影响。随后，我们转向ASE的反事实对应量（cf-ASE），给出了识别cf-ASE的充分条件集合，并提出了一种基于采样的实用估计算法。最后，我们通过一个基于模拟的测试平台（包括一个脓毒症管理环境）实验评估了cf-ASE的实用性。