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2020 – today
- 2024
[i14]Patrick Burauel, Frederick Eberhardt, Michel Besserve:
Controlling for discrete unmeasured confounding in nonlinear causal models. CoRR abs/2408.05647 (2024)
2010 – 2019
- 2019
- [c17]Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer
, Mark Junjie Li:
ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions. IJCAI 2019: 1488-1494 - [c16]Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern:
Approximate Causal Abstractions. UAI 2019: 606-615 - [p1]Frederick Eberhardt:
Beyond Cause-Effect Pairs. Cause Effect Pairs in Machine Learning 2019: 215-233 - [i13]Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer, Mark Junjie Li:
ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions. CoRR abs/1906.02385 (2019) - [i12]Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern:
Approximate Causal Abstraction. CoRR abs/1906.11583 (2019) - 2018
- [i11]Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt:
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes. CoRR abs/1804.02747 (2018) - 2017
- [j9]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
A constraint optimization approach to causal discovery from subsampled time series data. Int. J. Approx. Reason. 90: 208-225 (2017) - [j8]Frederick Eberhardt:
Introduction to the foundations of causal discovery. Int. J. Data Sci. Anal. 3(2): 81-91 (2017) - [c15]Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer:
SAT-Based Causal Discovery under Weaker Assumptions. UAI 2017 - [e1]Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva:
Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), Jersey City, USA, June 29, 2016. CEUR Workshop Proceedings 1792, CEUR-WS.org 2017 [contents] - 2016
- [j7]Frederick Eberhardt:
Green and grue causal variables. Synth. 193(4): 1029-1046 (2016) - [c14]Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona:
Multi-Level Cause-Effect Systems. AISTATS 2016: 361-369 - [c13]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. Probabilistic Graphical Models 2016: 216-227 - [c12]Krzysztof Chalupka, Tobias Bischoff, Frederick Eberhardt, Pietro Perona:
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data. UAI 2016 - [i10]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR abs/1602.07970 (2016) - [i9]Krzysztof Chalupka, Tobias Bischoff, Pietro Perona, Frederick Eberhardt:
Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data. CoRR abs/1605.09370 (2016) - [i8]Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona:
Estimating Causal Direction and Confounding of Two Discrete Variables. CoRR abs/1611.01504 (2016) - 2015
- [c11]Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt:
Visual Causal Feature Learning. UAI 2015: 181-190 - [c10]Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Do-calculus when the True Graph Is Unknown. UAI 2015: 395-404 - [i7]Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt:
Multi-Level Cause-Effect Systems. CoRR abs/1512.07942 (2015) - 2014
- [c9]Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo:
Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. UAI 2014: 340-349 - [i6]Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt:
Visual Causal Feature Learning. CoRR abs/1412.2309 (2014) - 2013
- [j6]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Experiment selection for causal discovery. J. Mach. Learn. Res. 14(1): 3041-3071 (2013) - [c8]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. UAI 2013 - [i5]Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo:
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. CoRR abs/1309.6836 (2013) - 2012
- [j5]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Learning linear cyclic causal models with latent variables. J. Mach. Learn. Res. 13: 3387-3439 (2012) - [c7]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. UAI 2012: 387-396 - [i4]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Noisy-OR Models with Latent Confounding. CoRR abs/1202.3735 (2012) - [i3]Frederick Eberhardt:
Almost Optimal Intervention Sets for Causal Discovery. CoRR abs/1206.3250 (2012) - [i2]Frederick Eberhardt, Clark Glymour, Richard Scheines:
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. CoRR abs/1207.1389 (2012) - [i1]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. CoRR abs/1210.4879 (2012) - 2011
- [j4]Frederick Eberhardt, David Danks:
Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. Minds Mach. 21(3): 389-410 (2011) - [j3]Frederick Eberhardt:
Reliability via synthetic a priori: Reichenbach's doctoral thesis on probability. Synth. 181(1): 125-136 (2011) - [c6]Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer:
Noisy-OR Models with Latent Confounding. UAI 2011: 363-372 - [r1]Frederick Eberhardt, Clark Glymour:
Hans Reichenbach's Probability Logic. Inductive Logic 2011: 357-389 - 2010
- [j2]Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang:
Actual causation: a stone soup essay. Synth. 175(2): 169-192 (2010) - [c5]Frederick Eberhardt:
Causal Discovery as a Game. NIPS Causality: Objectives and Assessment 2010: 87-96 - [c4]Frederick Eberhardt, Patrik O. Hoyer, Richard Scheines:
Combining Experiments to Discover Linear Cyclic Models with Latent Variables. AISTATS 2010: 185-192
2000 – 2009
- 2008
- [j1]Frederick Eberhardt:
A sufficient condition for pooling data. Synth. 163(3): 433-442 (2008) - [c3]Brent Bryan, Frederick Eberhardt, Christos Faloutsos:
Compact Similarity Joins. ICDE 2008: 346-355 - [c2]Frederick Eberhardt:
Almost Optimal Intervention Sets for Causal Discovery. UAI 2008: 161-168 - 2005
- [c1]Frederick Eberhardt, Clark Glymour, Richard Scheines:
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. UAI 2005: 178-184
Coauthor Index
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