想给概率逻辑程序加上精准的反事实推理？DeepSWIP用商WMC方法避免了DeepTwin的内生重复，实测快两倍多，做因果推断的朋友可以看看。
DeepSWIP为DeepProbLog引入单世界反事实语义，通过神经具体化将固定上下文神经谓词转为ProbLog选择，并应用单世界干预程序(SWIP)计算反事实。实验在MPI3D数据集上对比DeepTwin构造，针对12,000个查询实现2.14倍推理加速。SUMO HOV实验表明神经校准退化会偏误插件估计，而AIPW估计器可消除大部分一阶偏差。代码已开源。
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 standard quotient-WMC form of ProbLog conditionals identifies active neural probabilities and explains intervention cleaning, calibration sensitivity, and rare-evidence instability. Experiments on MPI3D confirm the transformation against a DeepTwin construction against 12,000 queries, as predicted and a 2.14$\times$ inference speedup from avoiding the Twin's endogenous duplication. A SUMO HOV experiment shows that neural calibration degradation biases plug-in estimates, while a correctly scoped randomized-policy AIPW estimator removes most first-order bias for population mean and ATE estimands. Code is at https://github.com/saibib/deep_SWIP.