This talk will give an overview of recent developments in (non-causal) probabilistic models that are tractable for computing properties such as marginal probabilities, entropies, expectations, and related queries of interest. These tractable probabilistic circuit models are now also effectively learned from data and can provide an efficient probabilistic foundation for causal inference algorithms.