<h1>Eberhardt, Frederick</h1> <h2>Combined from <a href="https://authors.library.caltech.edu">CaltechAUTHORS</a></h2> <ul> <li>Eberhardt, Frederick and Lee, Lin-Lin (2022) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20220309-981595000">Causal Emergence: When Distortions in a Map Obscure the Territory</a>; Philosophies; Vol. 7; No. 2; Art. No. 30; <a href="https://doi.org/10.3390/philosophies7020030">10.3390/philosophies7020030</a></li> <li>Eberhardt, Frederick (2022) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20220315-626013000">A contemporary example of Reichenbachian coordination</a>; Synthese; Vol. 200; No. 2; Art. No. 90; <a href="https://doi.org/10.1007/s11229-022-03571-8">10.1007/s11229-022-03571-8</a></li> <li>Thompson, W. H. and Esteban, O., el al. (2021) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20210831-204705170">Intracranial electrical stimulation alters meso-scale network integration as a function of network topology</a>; <a href="https://doi.org/10.1101/2021.01.16.426941">10.1101/2021.01.16.426941</a></li> <li>Dubois, Julien and Eberhardt, Frederick, el al. (2020) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20201109-135419046">Personality beyond taxonomy</a>; Nature Human Behaviour; Vol. 4; No. 11; 1110-1117; <a href="https://doi.org/10.1038/s41562-020-00989-3">10.1038/s41562-020-00989-3</a></li> <li>Dubois, Julien and Oya, Hiroyuki, el al. (2020) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20171115-073336929">Causal Mapping of Emotion Networks in the Human Brain: Framework and Initial Findings</a>; Neuropsychologia; Vol. 145; Art. No. 106571; PMCID PMC5949245; <a href="https://doi.org/10.1016/j.neuropsychologia.2017.11.015">10.1016/j.neuropsychologia.2017.11.015</a></li> <li>Beckers, Sander and Eberhardt, Frederick, el al. (2019) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20200527-100350364">Approximate Causal Abstraction</a>; Proceedings of Machine Learning Research; Vol. 115; 606-615; <a href="https://doi.org/10.48550/arXiv.1906.11583">10.48550/arXiv.1906.11583</a></li> <li>Zhalama, Mr. and Zhang, Jiji, el al. (2019) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20200527-101434154">ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions</a>; <a href="https://doi.org/10.48550/arXiv.1906.02385">10.48550/arXiv.1906.02385</a></li> <li>Chalupka, Krzysztof and Perona, Pietro, el al. (2018) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20180613-135346984">Fast Conditional Independence Test for Vector Variables with Large Sample Sizes</a>; <a href="https://doi.org/10.48550/arXiv.1804.02747">10.48550/arXiv.1804.02747</a></li> <li>Hyttinen, Antti and Plis, Sergey, el al. (2017) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20171109-074339105">A constraint optimization approach to causal discovery from subsampled time series data</a>; International Journal of Approximate Reasoning; Vol. 90; 208-225; <a href="https://doi.org/10.1016/j.ijar.2017.07.009">10.1016/j.ijar.2017.07.009</a></li> <li>Chalupka, Krzysztof and Eberhardt, Frederick, el al. (2017) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20170530-090152248">Causal Feature Learning: An Overview</a>; Behaviormetrika; Vol. 44; No. 1; 137-164; <a href="https://doi.org/10.1007/s41237-016-0008-2">10.1007/s41237-016-0008-2</a></li> <li>Chalupka, Krzysztof and Eberhardt, Frederick, el al. (2016) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190327-085917121">Estimating Causal Direction and Confounding of Two Discrete Variables</a>; <a href="https://doi.org/10.48550/arXiv.1611.01504">10.48550/arXiv.1611.01504</a></li> <li>Hyttinen, Antti and Plis, Sergey, el al. (2016) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20170221-090923083">Causal Discovery from Subsampled Time Series Data by Constraint Optimization</a>; Proceedings of Machine Learning Research; Vol. 52; 216-227; PMCID PMC5305170; <a href="https://doi.org/10.48550/arXiv.arXiv.1602.07970">10.48550/arXiv.arXiv.1602.07970</a></li> <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. (2016) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20190329-151702979">Multi-Level Cause-Effect Systems</a>; Proceedings of Machine Learning Research; Vol. 51; 361-369; <a href="https://doi.org/10.48550/arXiv.1512.07942">10.48550/arXiv.1512.07942</a></li> <li>Eberhardt, Frederick (2016) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20160602-123514680">Green and grue causal variables</a>; Synthese; Vol. 193; No. 4; 1029-1046; <a href="https://doi.org/10.1007/s11229-015-0832-z">10.1007/s11229-015-0832-z</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>Abrams, Marshall and Eberhardt, Frederick, el al. (2015) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20170616-075028154">Equidynamics and reliable reasoning about frequencies</a>; Metascience; Vol. 24; No. 2; 173-188; <a href="https://doi.org/10.1007/s11016-014-9971-y">10.1007/s11016-014-9971-y</a></li> <li>Eberhardt, Frederick (2014) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20141023-135945304">Direct Causes and the Trouble with Soft Interventions</a>; Erkenntnis; Vol. 79; No. 4; 755-777; <a href="https://doi.org/10.1007/s10670-013-9552-2">10.1007/s10670-013-9552-2</a></li> <li>Eberhardt, Frederick (2013) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20140227-092549193">Experimental Indistinguishability of Causal Structures</a>; Philosophy of Science; Vol. 80; No. 5; 684-696; <a href="https://doi.org/10.1086/673865">10.1086/673865</a></li> <li>Hyttinen, Antti and Eberhardt, Frederick, el al. (2013) <a href="https://resolver.caltech.edu/CaltechAUTHORS:20140123-114755984">Experiment Selection for Causal Discovery</a>; Journal of Machine Learning Research; Vol. 14; 3041-3071</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>