PAC Learning of Causal Trees with Latent Variables

Learning causal probabilistic models with latent variables from observational and experimental data is an important problem. In this paper we present a polynomial-time algorithm that PAC-learns the structure and parameters of a rooted, tree-structured causal network of bounded degree where the internal nodes of the tree cannot be observed or manipulated. Our algorithm is the first of its kind to provably learn the structure and parameters of tree-structured causal models with latent internal variables from random examples and active experiments.