Frederick Eberhardt

49 papers A* 2A 14Journal 27Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	When the Coffee Feature Activates on Coffins: An Analysis of Feature Extraction and Steering for Mechanistic Interpretability. CoRR Raphael Ronge, Markus Maier, Frederick Eberhardt
2025	—	`conf`	Controlling for discrete unmeasured confounding in nonlinear causal models. CLeaR Patrick Burauel, Frederick Eberhardt, Michel Besserve
2025	J	`jnl`	Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity. Manag. Sci. Frederick Eberhardt, Nur Kaynar, Auyon Siddiq
2025	A	`conf`	Lower Bounds on the Size of Markov Equivalence Classes. UAI Erik Jahn, Frederick Eberhardt, Leonard J. Schulman
2025	J	`jnl`	Lower Bounds on the Size of Markov Equivalence Classes. CoRR Erik Jahn, Frederick Eberhardt, Leonard J. Schulman
2025	J	`jnl`	Modeling Discrimination with Causal Abstraction. CoRR Milan Mossé, Kara Schechtman, Frederick Eberhardt, Thomas Icard
2024	J	`jnl`	Controlling for discrete unmeasured confounding in nonlinear causal models. CoRR Patrick Burauel, Frederick Eberhardt, Michel Besserve
2019	A*	`conf`	ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions. IJCAI Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer, Mark Junjie Li
2019	J	`jnl`	ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions. CoRR Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer, Mark Junjie Li
2019	J	`jnl`	Approximate Causal Abstraction. CoRR Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern
2019	A	`conf`	Approximate Causal Abstractions. UAI Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern
2019	—	`ch.`	Beyond Cause-Effect Pairs. Cause Effect Pairs in Machine Learning Frederick Eberhardt
2018	J	`jnl`	Fast Conditional Independence Test for Vector Variables with Large Sample Sizes. CoRR Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
2017	J	`jnl`	A constraint optimization approach to causal discovery from subsampled time series data. Int. J. Approx. Reason. Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
2017	J	`jnl`	Introduction to the foundations of causal discovery. Int. J. Data Sci. Anal. Frederick Eberhardt
2017	—	`ed.`	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. CFA@UAI Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva
2017	A	`conf`	SAT-Based Causal Discovery under Weaker Assumptions. UAI Zhalama, Jiji Zhang, Frederick Eberhardt, Wolfgang Mayer
2016	—	`conf`	Causal Discovery from Subsampled Time Series Data by Constraint Optimization. Probabilistic Graphical Models Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
2016	J	`jnl`	Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
2016	J	`jnl`	Estimating Causal Direction and Confounding of Two Discrete Variables. CoRR Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona
2016	J	`jnl`	Green and grue causal variables. Synth. Frederick Eberhardt
2016	A	`conf`	Multi-Level Cause-Effect Systems. AISTATS Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona
2016	A	`conf`	Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data. UAI Krzysztof Chalupka, Tobias Bischoff, Frederick Eberhardt, Pietro Perona
2016	J	`jnl`	Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data. CoRR Krzysztof Chalupka, Tobias Bischoff, Pietro Perona, Frederick Eberhardt
2015	A	`conf`	Do-calculus when the True Graph Is Unknown. UAI Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo
2015	J	`jnl`	Multi-Level Cause-Effect Systems. CoRR Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
2015	A	`conf`	Visual Causal Feature Learning. UAI Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
2014	A	`conf`	Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. UAI Antti Hyttinen, Frederick Eberhardt, Matti Järvisalo
2014	J	`jnl`	Visual Causal Feature Learning. CoRR Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
2013	A	`conf`	Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. UAI Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo
2013	J	`jnl`	Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. CoRR Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt, Matti Järvisalo
2013	J	`jnl`	Experiment selection for causal discovery. J. Mach. Learn. Res. Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2012	J	`jnl`	Almost Optimal Intervention Sets for Causal Discovery CoRR Frederick Eberhardt
2012	J	`jnl`	Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables CoRR Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2012	A	`conf`	Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. UAI Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2012	J	`jnl`	Learning linear cyclic causal models with latent variables. J. Mach. Learn. Res. Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2012	J	`jnl`	Noisy-OR Models with Latent Confounding CoRR Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2012	J	`jnl`	On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables CoRR Frederick Eberhardt, Clark Glymour, Richard Scheines
2011	J	`jnl`	Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. Minds Mach. Frederick Eberhardt, David Danks
2011	—	`ch.`	Hans Reichenbach's Probability Logic. Inductive Logic Frederick Eberhardt, Clark Glymour
2011	A	`conf`	Noisy-OR Models with Latent Confounding. UAI Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
2011	J	`jnl`	Reliability via synthetic a priori: Reichenbach's doctoral thesis on probability. Synth. Frederick Eberhardt
2010	J	`jnl`	Actual causation: a stone soup essay. Synth. Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
2010	—	`conf`	Causal Discovery as a Game. NIPS Causality: Objectives and Assessment Frederick Eberhardt
2010	A	`conf`	Combining Experiments to Discover Linear Cyclic Models with Latent Variables. AISTATS Frederick Eberhardt, Patrik O. Hoyer, Richard Scheines
2008	J	`jnl`	A sufficient condition for pooling data. Synth. Frederick Eberhardt
2008	A	`conf`	Almost Optimal Intervention Sets for Causal Discovery. UAI Frederick Eberhardt
2008	A*	`conf`	Compact Similarity Joins. ICDE Brent Bryan, Frederick Eberhardt, Christos Faloutsos
2005	A	`conf`	On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. UAI Frederick Eberhardt, Clark Glymour, Richard Scheines