DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs

Standard machine learning tells us what is likely given what we observe, but many real-world decisions demand something more: understanding what would have happened under different circumstances. Counterfactual reasoning is essential for fairness auditing, policy evaluation, and causal explanation. DeepSWIP extends DeepProbLog — a framework blending neural perception with logical reasoning — to support principled counterfactual inference, without the computational overhead of maintaining parallel "twin" worlds. Potential applications include fairness analysis in automated decision-making, causal attribution in scientific discovery pipelines, and auditing of AI-assisted systems where understanding alternative outcomes is a regulatory or ethical requirement.