A Unifying Framework for Causal Explanation of Sequential Decision Making
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We present a novel framework for causal explanations of stochastic, sequential decision-making systems. Building on the well-studied structural causal model paradigm for causal reasoning, we show how to identify semantically distinct types of explanations for agent actions using a single unified approach. We provide results on the generality of this framework, run time bounds, and offer several approximate techniques. Finally, we discuss several qualitative scenarios that illustrate the framework's flexibility and efficacy.
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