Abstract
Probabilistic circuits are a unifying class of representations of probability distributions, which admit polynomial (often linear) time exact algorithms for many probabilistic queries. However, not all information about a system can be captured through its probability distribution: in particular, causal effects cannot be expressed in purely probabilistic language. To this end, Pearl's Causal Hierarchy defines a much richer query language through the semantics of intervention. In this talk I will describe recent progress in computational aspects of causal reasoning; focusing in particular on the identification of probabilistic and logical circuit representations that admit efficient algorithms for causal queries.