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Causal Bayesian networks are 'causal' models since they make predictions about interventional distributions.
Implication and the algebra of logic
Clarence Irving Lewis · 1912
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Use and abuse of regression
George Box · 1966
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A theory of conditionals
Robert Stalnaker · 1968
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Counterfactuals
David Lewis · 1973
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Critical notice of lewis’ counterfactuals
Kit Fine · 1975
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Counterfactual dependence and time’s arrow
David Lewis · 1979
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Autonomy
John Aldrich · 1989
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Nonmonotonic reasoning and causation
Yoav Shoham · 1990
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An axiomatic characterization of causal counterfactuals
David Galles and Judea Pearl · 1998
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Causal inference without counterfactuals
Philip Dawid · 2000
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Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2001
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Counterfactuals in a Dynamic Context
Kai von Fintel · 2001
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Causal inference of ambiguous manipulations
Peter Spirtes and Richard Scheines · 2004
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Making things happen: A theory of causal explanation
James Woodward · 2005
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N-1 experiments suffice to determine the causal relations among n variables
Frederick Eberhardt, Clark Glymour, and Richard Scheines · 2006
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Hunting causes and using them: Approaches in philosophy and economics
Nancy Cartwright · 2007
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Counterfactual scorekeeping
Anthony S. Gillies · 2007
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What counterfactuals can be tested
Ilya Shpitser and Judea Pearl · 2007
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Interdefining causation and intervention
Michael Baumgartner · 2009
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Causality
Judea Pearl · 2009
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Beware of the DAG!
Philip Dawid · 2010
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Causal inference using the algorithmic Markov condition
Dominik Janzing and Bernhard Schölkopf · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Quantifying causal emergence shows that macro can beat micro
Erik P. Hoel, Larissa Albantakis, and Giulio Tononi · 2013
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Commentary: race and sex are causes
Clark Glymour and Madelyn R Glymour · 2014
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Most Counterfactuals Are False
Alan Hajek · 2014
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Visual causal feature learning
Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt · 2015
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Green and grue causal variables
Frederick Eberhardt · 2016
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Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference
Dominik Janzing, Rafael Chaves, and Bernhard Schölkopf · 2016
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Causal inference in statistics: A primer
Judea Pearl, Madelyn Glymour, and Nicholas P Jewell · 2016
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MERLiN: Mixture Effect Recovery in Linear Networks
Sebastian Weichwald, Moritz Grosse-Wentrup, and Arthur Gretton · 2016
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The problem of variable choice
James Woodward · 2016
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When the map is better than the territory
Erik P. Hoel · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Causal consistency of structural equation models
Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, and Bernhard Schölkopf · 2017
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Does obesity shorten life? Or is it the soda? On non-manipulable causes
Judea Pearl · 2018
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Abstracting causal models
Sander Beckers and Joseph Y. Halpern · 2019
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Lectures on Graphical Models, 3rd edition
Steffen Lauritzen · 2019
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On the interpretation of do(x)
Judea Pearl · 2019
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Identifiability results for multimodal contrastive learning
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, and Julia E. Vogt · 2023
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Comparing causal frameworks: Potential outcomes, structural models, graphs, and abstractions
Duligur Ibeling and Thomas Icard · 2023
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Synergies between disentanglement and sparsity: Generalization and identifiability in multi-task learning
Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, and Quentin Bertrand · 2023
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Causal abstraction with soft interventions
Riccardo Massidda, Atticus Geiger, Thomas Icard, and Davide Bacciu · 2023
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A measure-theoretic axiomatisation of causality
Junhyung Park, Simon Buchholz, Bernhard Schölkopf, and Krikamol Muandet · 2023
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Linear causal disentanglement via interventions
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Boxes and Diamonds: An Open Introduction to Modal Logic
Richard Zach · 2019
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Approximate causal abstractions
Sander Beckers, Frederick Eberhardt, and Joseph Y. Halpern · 2020
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Invariance, causality and robustness
Peter Bühlmann · 2020
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What's sex got to do with machine learning?
Lily Hu and Issa Kohler-Hausmann · 2020
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Probabilistic reasoning across the causal hierarchy
Duligur Ibeling and Thomas Icard · 2020
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Causal feature learning for utility-maximizing agents
David Kinney and David Watson · 2020
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Chandler Squires, Anna Seigal, Salil S Bhate, and Caroline Uhler · 2023
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Causation and Manipulability
James Woodward · 2023
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Invariance & causal representation learning: Prospects and limitations
Simon Bing, Tom Hochsprung, Jonas Wahl, Urmi Ninad, and Jakob Runge · 2024
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Learning linear causal representations from interventions under general nonlinear mixing
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, and Pradeep Ravikumar · 2024
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Smoke and mirrors in causal downstream tasks
Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco Locatello · 2024
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Efficient adjustment for complex covariates: Gaining efficiency with DOPE
Alexander Mangulad Christgau and Niels Richard Hansen · 2024
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Interventionally consistent surrogates for complex simulation models
Joel Dyer, Nicholas George Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Ani Calinescu, Theodoros Damoulas, and Michael J. Wooldridge · 2024
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A phenomenological account for causality in terms of elementary actions
Dominik Janzing and Sergio H. G. Mejia · 2024
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Learning causal representations from general environments: Identifiability and intrinsic ambiguity
Jikai Jin and Vasilis Syrgkanis · 2024
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Targeted reduction of causal models
Armin Kekić, Bernhard Schölkopf, and Michel Besserve · 2024
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Disentangled representation learning in non-Markovian causal systems
Adam Li, Yushu Pan, and Elias Bareinboim · 2024
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Position: The causal revolution needs scientific pragmatism
Joshua Loftus · 2024
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All or none: Identifiable linear properties of next-token predictors in language modeling
Emanuele Marconato, Sébastien Lachapelle, Sebastian Weichwald, and Luigi Gresele · 2024
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Process theory of causality: a category-theoretic perspective
Jun Otsuka and Hayato Saigo · 2024
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Counterfactual realizability
Arvind Raghavan and Elias Bareinboim · 2024
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Axiomatization of interventional probability distributions
Kayvan Sadeghi and Terry Soo · 2024
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Identifying representations for intervention extrapolation
Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Kumar Ravikumar, Niklas Pfister, and Jonas Peters · 2024
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General identifiability and achievability for causal representation learning
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, and Ali Tajer · 2024
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Nonparametric identifiability of causal representations from unknown interventions
Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David Blei, and Bernhard Schölkopf · 2024
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Identifiability guarantees for causal disentanglement from purely observational data
Ryan Welch, Jiaqi Zhang, and Caroline Uhler · 2024
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Causal component analysis
Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, and Bernhard Schölkopf · 2024
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Neural causal abstractions
Kevin Xia and Elias Bareinboim · 2024
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A sparsity principle for partially observable causal representation learning
Danru Xu, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, and Sara Magliacane · 2024
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Multi-view causal representation learning with partial observability
Dingling Yao, Danru Xu, Sebastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, and Francesco Locatello · 2024
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Causally abstracted multi-armed bandits
Fabio Massimo Zennaro, Nicholas George Bishop, Joel Dyer, Yorgos Felekis, Ani Calinescu, Michael J. Wooldridge, and Theodoros Damoulas · 2024
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Identifiability guarantees for causal disentanglement from soft interventions
Jiaqi Zhang, Kristjan Greenewald, Chandler Squires, Akash Srivastava, Karthikeyan Shanmugam, and Caroline Uhler · 2024
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Meaningful causal aggregation and paradoxical confounding
Yuchen Zhu, Kailash Budhathoki, Jonas M Kübler, and Dominik Janzing · 2024
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