Frederick Eberhardt

Joseph Y. Halpern

CoRR, 2019

Approximate Causal Abstractions.

[BibT_eX]

[DOI]

Sander Beckers

Joseph Y. Halpern

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Beyond Cause-Effect Pairs.

[BibT_eX]

[DOI]

Proceedings of the Cause Effect Pairs in Machine Learning, 2019

2018

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Introduction to the foundations of causal discovery.

[BibT_eX]

[DOI]

Int. J. Data Sci. Anal., 2017

A constraint optimization approach to causal discovery from subsampled time series data.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

SAT-Based Causal Discovery under Weaker Assumptions.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016

Green and grue causal variables.

[BibT_eX]

[DOI]

Synth., 2016

Estimating Causal Direction and Confounding of Two Discrete Variables.

[BibT_eX]

[DOI]

CoRR, 2016

Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Causal Discovery from Subsampled Time Series Data by Constraint Optimization.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Multi-Level Cause-Effect Systems.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Do-calculus when the True Graph Is Unknown.

[BibT_eX]

[DOI]

Matti Järvisalo

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Visual Causal Feature Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming.

[BibT_eX]

[DOI]

Matti Järvisalo

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

2013

Experiment selection for causal discovery.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2013

Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

2012

Learning linear cyclic causal models with latent variables.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2012

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011

Hans Reichenbach's Probability Logic.

[BibT_eX]

[DOI]

Clark Glymour

Proceedings of the Inductive Logic, 2011

Reliability via synthetic a priori: Reichenbach's doctoral thesis on probability.

[BibT_eX]

[DOI]

Synth., 2011

Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models.

[BibT_eX]

[DOI]

David Danks

Minds Mach., 2011

Noisy-OR Models with Latent Confounding.

[BibT_eX]

[DOI]

Proceedings of the UAI 2011, 2011

2010

Actual causation: a stone soup essay.

[BibT_eX]

[DOI]

Synth., 2010

Combining Experiments to Discover Linear Cyclic Models with Latent Variables.

[BibT_eX]

[DOI]

Richard Scheines

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Causal Discovery as a Game.

[BibT_eX]

[DOI]

Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

2008

A sufficient condition for pooling data.

[BibT_eX]

[DOI]

Synth., 2008

Almost Optimal Intervention Sets for Causal Discovery.

[BibT_eX]

[DOI]

Proceedings of the UAI 2008, 2008

Compact Similarity Joins.

[BibT_eX]

[DOI]

Brent Bryan

Christos Faloutsos

Proceedings of the 24th International Conference on Data Engineering, 2008

2005

On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.

[BibT_eX]

[DOI]