TY - JOUR AU - Tadepalli, Prasad AU - Russell, Stuart J. PY - 2021/05/18 Y2 - 2024/03/28 TI - PAC Learning of Causal Trees with Latent Variables JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 11 SE - AAAI Technical Track on Machine Learning IV DO - 10.1609/aaai.v35i11.17175 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17175 SP - 9774-9781 AB - Learning causal 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. ER -