PAC Learning of Causal Trees with Latent Variables
Abstract
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.
Cite
Text
Tadepalli and Russell. "PAC Learning of Causal Trees with Latent Variables." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I11.17175Markdown
[Tadepalli and Russell. "PAC Learning of Causal Trees with Latent Variables." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/tadepalli2021aaai-pac/) doi:10.1609/AAAI.V35I11.17175BibTeX
@inproceedings{tadepalli2021aaai-pac,
title = {{PAC Learning of Causal Trees with Latent Variables}},
author = {Tadepalli, Prasad and Russell, Stuart J.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {9774-9781},
doi = {10.1609/AAAI.V35I11.17175},
url = {https://mlanthology.org/aaai/2021/tadepalli2021aaai-pac/}
}