Single World Intervention Programs

DeepSWIP: Counterfactual reasoning for neural probabilistic logic programs via quotient-WMC

DeepSWIP introduces a single-world counterfactual semantics for DeepProbLog, enabling causal inference over neurosymbolic programs that combine neural perception with probabilistic logic. The approach uses neural materialization to reduce neural predicates to standard ProbLog choices, then applies Single World Intervention Programs (SWIPs) and weighted model counting to compute exact counterfactuals from a single transformed program. Experiments on MPI3D validate the method against a DeepTwin construction across 12,000 queries and show a 2.14× inference speedup, while a SUMO HOV experiment demonstrates that neural calibration degradation biases plug-in causal estimates and that a correctly scoped AIPW estimator removes most first-order bias.