Causal Logic Models

Despite their success in transferring the powerful human faculty of causal reasoning to a mathematical and computational form, causal models have not been widely used in the context of core AI applications such as robotics. In this paper, we argue that this discrepancy is due to the static, propositional nature of existing causality formalisms that make them difficult to apply in dynamic real-world situations where the variables of interest are not necessarily known a priori. We define Causal Logic Models (CLMs), a new probabilistic, first-order representation which uses causality as a fundamental building block. Rather than merely converting causal rules to first-order logic as various methods in Statistical Relational Learning have done, we treat the causal rules as basic primitives which cannot be altered without changing the system. We provide sketches of algorithms for causal reasoning using CLMs, preliminary results for causal explanation, and explore the significant differences between causal reasoning in CLMs and fixed causal graphs, including the non-locality of manipulation and the non-commutability between observation and manipulation.