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DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs

Habib 2026-06-18
Saimun HabibVaishak BelleFengxiang He

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 ord

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Key Contributions

  • 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.

Research Themes

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