arXiv cs.AIPaper
A Computationally Feasible Framework for Causal Probabilistic Explanation
This bridges a real gap between causal rigor and computational scale, but the excerpt doesn't show evidence it works on real-world models or that practitioners will adopt it over faster attribution methods. Wait for empirical validation on large models before considering it.