Why am I stuck? Causal Logic Models for Token-Level Causal Reasoning

We present a new approach to token-level causal reasoning that we call Causal Logic Models (CLMs). A CLM is a first-order represen-tation that allows one to produce token-level causal explanations or predictions in domains which are too vast to generate a complete causal model. CLMs produce explanations/predictions as a dynamic sequence of mechanisms (SoMs) that chain together to propagate causal influ-ence through time. We argue that explana-tions/predictions of this form are more fun-damental than explanations that involve likely states of variables in conjunction with a complete causal model. We compare this approach to the causal explanations of Halpern and Pearl [2005], and show that even on relatively simple real-world physical systems, their method of generat-ing explanations can quickly become intractable. We argue that the SoMs approach is qualitatively closer to the human causal reasoning process, and that for many real problems in AI such as diagnosing why a robot is stuck, CLMs provide more tractable and informative explanations.