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Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences.
Equivalence and Synthesis of Causal Models
Thomas Verma and Judea Pearl · 1991
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Causation, Prediction, and Search
Peter Spirtes, Clark N. Glymour, Richard Scheines, David Heckerman, Christopher Meek, Gregory Cooper, and Thomas Richardson · 2000
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Finding Optimal Bayesian Networks
David Maxwell Chickering and Christopher Meek · 2002
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs
Silvia Acid and Luis M. de Campos · 2003
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Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations
Ioannis Tsamardinos, Constantin F. Aliferis, and Alexander Statnikov · 2003
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Algorithms for Large Scale Markov Blanket Discovery
Ioannis Tsamardinos, Constantin F. Aliferis, and Alexander R. Statnikov · 2003
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A Linear Non-Gaussian Acyclic Model for Causal Discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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Optimal transport: old and new
Cédric Villani · 2008
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A Comparison of Structural Distance Measures for Causal Bayesian Network Models
Martijn de Jongh and Marek J Druzdzel · 2009
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Causality: Models, Reasoning and Inference
Judea Pearl · 2009
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Hilbert space embeddings and metrics on probability measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert R.G. Lanckriet · 2010
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Concave Penalized Estimation of Sparse Gaussian Bayesian Networks
Bryon Aragam and Qing Zhou · 2015
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Structural intervention distance for evaluating graphs
Jonas Martin Peters and Peter Bühlmann · 2015
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Comparative benchmarking of causal discovery techniques
Karamjit Singh, Garima Gupta, Vartika Tewari, and Gautam Shroff · 2017
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Learning and testing causal models with interventions
Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, and Saravanan Kandasamy · 2018
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The Book of Why
Judea Pearl and Dana Mackenzie · 2018
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Invariant models for transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
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The case for evaluating causal models using interventional measures and empirical data
Amanda Gentzel, Dan Garant, and David Jensen · 2019
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Causal discovery toolbox: Uncover causal relationships in python, 2019
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Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Martin Peters, Dominik Janzing, and Bernard Schölkopf · 2017
Cited alongside, same era.
Diviyan Kalainathan and Olivier Goudet · 2019
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The seven tools of inference, with reflections on machine learning
Judea Pearl · 2019
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Bayesian Network Repository
Gal Elidan · 2021
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