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：反事实推断考虑在一个平行世界中进行假设性干预，该平行世界与事实世界共享部分证据。当证据指定了流形上的条件分布时，反事实推断可能难以解析求解。我们提出了一种从反事实分布中模拟值的算法，该算法可对离散和连续变量设置条件。我们证明，所提出的算法可表述为粒子滤波器，从而产生渐近有效的推断。该算法已被应用于信用评分中的公平性分析。