From probability to causality in probabilistic logic programming
The episode discusses a paper on whether probabilistic logic programs learned from data can answer causal questions. The hosts explain that only sometimes, depending on orientability of dependency graphs. They introduce causal symmetries from relational structure, which can orient edges that standard Bayesian network methods cannot, enabling interventional reasoning.