Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables. We show that the proposed algorithm can be presented as a particle filter leading to asymptotically valid inference. The algorithm is applied to fairness analysis in credit scoring.

翻译：反事实推理考虑在一个与事实世界共享某些证据的平行世界中进行假设性干预。如果证据指定了流形上的条件分布，反事实可能无法通过解析方法计算。我们提出了一种算法，用于从反事实分布中模拟数值，其中条件可设定于离散和连续变量上。我们证明，所提出的算法可表示为粒子滤波器，从而得到渐近有效的推理。该算法应用于信用评分中的公平性分析。