DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs
Neurosymbolic systems such as DeepProbLog combine neural perception with probabilistic logic, but standard inference is associational. Counterfactual reasoning additionally requires a causal semantics for interventions and evidence. We introduce DeepSWIP, a single-world counterfactual semantics for DeepProbLog programs. Using neural materialization, we reduce fixed-context neural predicates to ordinary ProbLog choices, apply Single World Intervention Programs (SWIPs), and compute counterfactuals by weighted model counting (WMC) over a single transformed program. Under finite grounding and unique-supported-model assumptions, DeepSWIP is exact relative to the learned materialized FCM. The s...
| Authors | Saimun Habib, Vaishak Belle, Fengxiang He |
|---|---|
| Primary category | cs.AI |
| Published | 18 June 2026 |
| Last updated at source | 19 June 2026 |
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