<h1>Eberhardt, Frederick</h1>
<h2>Book Chapter from <a href="https://authors.library.caltech.edu">CaltechAUTHORS</a></h2>
<ul>
<li>Chalupka, Krzysztof and Bischoff, Tobias, el al. (2016) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20170530-090152140">Unsupervised Discovery of El Niño Using Causal Feature Learning on Microlevel Climate Data</a>; ISBN 978-0-9966431-1-5; Uncertainty in Artificial Intelligence. Proceedings of the Thirty-Second Conference (2016); 72-81; <a href="https://doi.org/10.48550/arXiv.1605.09370">10.48550/arXiv.1605.09370</a></li>
<li>Chalupka, Krzysztof and Perona, Pietro, el al. (2015) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085913684">Visual Causal Feature Learning</a>; ISBN 978-0-9966431-0-8; UAI'15 Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence; 181-190; <a href="https://doi.org/10.48550/arXiv.1412.2309">10.48550/arXiv.1412.2309</a></li>
<li>Hyttinen, Antti and Hoyer, Patrik O., el al. (2013) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085903347">Discovering cyclic causal models with latent variables: a general SAT-based procedure</a>; ISBN 9780974903996; UAI'13 Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence; 301-310; <a href="https://doi.org/10.48550/arXiv.1309.6836">10.48550/arXiv.1309.6836</a></li>
<li>Hyttinen, Antti and Eberhardt, Frederick, el al. (2012) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085855919">Causal discovery of linear cyclic models from multiple experimental data sets with overlapping variables</a>; ISBN 978-0-9749039-8-9; UAI'12 Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence; 387-396; <a href="https://doi.org/10.48550/arXiv.1210.4879">10.48550/arXiv.1210.4879</a></li>
<li>Eberhardt, Frederick and Glymour, Clark, el al. (2012) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085852486">On the number of experiments sufficient and in the worst case necessary to identify all causal relations among N variables</a>; ISBN 0-9749039-1-4; UAI'05 Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence; 178-184; <a href="https://doi.org/10.48550/arXiv.1207.1389">10.48550/arXiv.1207.1389</a></li>
<li>Hyttinen, Antti and Eberhardt, Frederick, el al. (2011) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085849059">Noisy-OR Models with Latent Confounding</a>; ISBN 978-0-9749039-7-2; UAI'11 Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence; 363-372; <a href="https://doi.org/10.48550/arXiv.1202.3735v1">10.48550/arXiv.1202.3735v1</a></li>
<li>Eberhardt, Frederick (2008) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085859738">Almost Optimal Intervention Sets for Causal Discovery</a>; ISBN 0-9749039-4-9; UAI'08 Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence; 161-168; <a href="https://doi.org/10.48550/arXiv.1206.3250">10.48550/arXiv.1206.3250</a></li>
</ul>