Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Embracing events in causal modelling: interventions and counterfactuals in CP-ogic
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
Causes and explanations: a structural-model approach: part i: causes
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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We present a new approach to token-level causal reasoning that we call Sequences Of Mechanisms (SoMs), which models causality as a dynamic sequence of active mechanisms that chain together to propagate causal influence through time. We motivate this approach by using examples from AI and robotics and show why existing approaches are inadequate. We present an algorithm for causal reasoning based on SoMs, which takes as input a knowledge base of first-order mechanisms and a set of observations, and it hypothesizes which mechanisms are active at what time. We show empirically that our algorithm produces plausible causal explanations of simulated observations generated from a causal model. We argue that the SoMs approach is qualitatively closer to the human causal reasoning process, for example, it will only include relevant variables in explanations. We present new insights about causal reasoning that become apparent with this view. One such insight is that observation and manipulation do not commute in causal models, a fact which we show to be a generalization of the Equilibration-Manipulation Commutability of [Dash(2005)].