Phoster

Research and Development

Causal Machine Learning

Introduction

Toward Causal Representation Learning by Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan R. Ke, Nal Kalchbrenner, Anirudh Goyal and Yoshua Bengio and Causality for Machine Learning by Bernhard Schölkopf.

Causality

Causality: Models, Reasoning, and Inference by Judea Pearl, On the Logic of Causal Models by Dan Geiger and Judea Pearl, The Oxford Handbook of Causal Reasoning edited by Michael Waldmann, Elements of Causal Inference: Foundations and Learning Algorithms by Jonas Peters, Dominik Janzing and Bernhard Schölkopf and Statistics and Causal Inference by Paul W. Holland.

Counterfactual Reasoning

Counterfactuals and Causal Inference: Methods and Principles for Social Research by Stephen L. Morgan and Christopher Winship and A Causal Theory of Counterfactuals by Eric Hiddleston.

Interventional Reasoning

Causation and Intervention by Frederick Eberhardt, Interventions and Causal Inference by Frederick Eberhardt and Richard Scheines, Inferring Interventional Predictions from Observational Learning Data by Björn Meder, York Hagmayer and Michael R. Waldmann and Experiment Selection for Causal Discovery by Antti Hyttinen, Frederick Eberhardt and Patrik O. Hoyer.

Action Model Learning

A Review of Machine Learning for Automated Planning by Sergio Jiménez, Tomás de la Rosa, Susana Fernández, Fernando Fernández and Daniel Borrajo, Learning-assisted Automated Planning: Looking Back, Taking Stock, Going Forward by Terry Zimmerman and Subbarao Kambhampati, Machine Learning Methods for Planning edited by Steven Minton, Learning Macro-actions for Arbitrary Planners and Domains by Muhammad A. H. Newton, John Levine, Maria Fox and Derek Long, Generation of Macro-operators via Investigation of Action Dependencies in Plans by Lukáš Chrpa, Autonomous Learning of Action Models for Planning by Neville Mehta, Prasad Tadepalli and Alan Fern, Efficient Learning of Action Models for Planning by Neville Mehta, Prasad Tadepalli and Alan Fern, Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains by Yolanda Gil, Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition by Xuemei Wang, Learning Planning Operators in Real-world, Partially Observable Environments by Matthew D. Schmill, Tim Oates and Paul R. Cohen, Inductive Learning of Reactive Action Models by Scott Benson, Learning Partially Observable Deterministic Action Models by Eyal Amir and Allen Chang, Planning While Learning Operators by Xuemei Wang and An Integrated Approach of Learning, Planning, and Execution by Ramón García-Martínez and Daniel Borrajo.

Model Learning

Learning Predictive Models from Observation and Interaction by Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine and Chelsea Finn, Causal Discovery in Physical Systems from Videos by Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox and Animesh Garg, Interaction Networks for Learning about Objects, Relations and Physics by Peter Battaglia, Razvan Pascanu, Matthew Lai and Danilo J. Rezende, Visual Interaction Networks: Learning a Physics Simulator from Video by Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu and Andrea Tacchetti, Unsupervised Intuitive Physics from Visual Observations by Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra and Andrea Vedaldi, End-to-end Differentiable Physics for Learning and Control by Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum and J. Zico Kolter, Neural Relational Inference for Interacting Systems by Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling and Richard Zemel, Physics-as-inverse-graphics: Joint Unsupervised Learning of Objects and Physics from Video by Miguel Jaques, Michael Burke and Timothy Hospedales, Physics-as-inverse-graphics: Unsupervised Physical Parameter Estimation from Video by Miguel Jaques, Michael Burke and Timothy Hospedales and Flexible Neural Representation for Physics Prediction by Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F. Fei-Fei, Josh Tenenbaum and Daniel L. Yamins.

Machine Learning

Causal Generative Neural Networks by Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz and Michèle Sebag, Learning Functional Causal Models with Generative Neural Networks by Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz and Michèle Sebag, The Causal-neural Connection: Expressiveness, Learnability, and Inference by Kevin Xia, Kai-Zhan Lee, Yoshua Bengio and Elias Bareinboim, Relating Graph Neural Networks to Structural Causal Models by Matej Zečević, Devendra S. Dhami, Petar Veličković and Kristian Kersting, Reinforcement Learning and Causal Models by Samuel J. Gershman and Causal Reinforcement Learning Using Observational and Interventional Data by Maxime Gasse, Damien Grasset, Guillaume Gaudron and Pierre-Yves Oudeyer.