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Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors.
Probabilistic evaluation of counterfactual queries
Alexander Balke and Judea Pearl · 1994
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Identification and estimation of local average treatment effects
Guido W. Imbens and Joshua D. Angrist · 1994
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Bounds on treatment effects from studies with imperfect compliance
Alexander Balke and Judea Pearl · 1997
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Monotonicity hints
Joseph Sill and Yaser S Abu-Mostafa · 1997
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Probabilities of causation: three counterfactual interpretations and their identification
Judea Pearl · 1999
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Probabilities of causation: Bounds and identification
Jin Tian and Judea Pearl · 2000
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Causality (2nd edition)
Judea Pearl · 2009
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International stroke trial database (version 2)
Peter Sandercock and Anna. Niewada, Maciej; Czlonkowska · 2011
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Causal responsibility and counterfactuals
David A Lagnado, Tobias Gerstenberg, and Ro’i Zultan · 2013
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Replication data for: Spring Cleaning: Rural Water Impacts, Valuation, and Property Rights Institutions
Michael Kremer, Jessica Leino, Edward Miguel, and Alix Peterson · 2015
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Monotonic calibrated interpolated look-up tables
Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, and Alexander Van Esbroeck · 2016
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Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2016
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Deep counterfactual networks with propensity-dropout
Ahmed M Alaa, Michael Weisz, and Mihaela Van Der Schaar · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Counterfactual fairness
MJ Kusner, J Loftus, Christopher Russell, and R Silva · 2017
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Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
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Learning functional causal models with generative neural networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michele Sebag · 2018
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A non-parametric projection-based estimator for the probability of causation, with application to water sanitation in kenya
Maria Cuellar and Edward H Kennedy · 2020
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Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel C Castro, and Ben Glocker · 2020
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Improving the accuracy of medical diagnosis with causal machine learning
Jonathan G Richens, Ciarán M Lee, and Saurabh Johri · 2020
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Counterexample-guided learning of monotonic neural networks
Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, and Guy Van den Broeck · 2020
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Applying class-to-class siamese networks to explain classifications with supportive and contrastive cases
Xiaomeng Ye, David Leake, William Huibregtse, and Mehmet Dalkilic · 2020
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Ganite: Estimation of individualized treatment effects using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar · 2018
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Unit selection based on counterfactual logic
Judea Pearl Ang Li · 2019
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Copy, paste, infer: A robust analysis of twin networks for counterfactual inference
Logan Graham, Ciarán M Lee, and Yura Perov · 2019
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Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
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Counterfactual off-policy evaluation with gumbel-max structural causal models
Michael Oberst and David Sontag · 2019
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Adapting neural networks for the estimation of treatment effects
Claudia Shi, David M Blei, and Victor Veitch · 2019
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Bounding causal effects on continuous outcomes
Junzhe Zhang and Elias Bareinboim · 2020
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Explaining black-box algorithms using probabilistic contrastive counterfactuals
Sainyam Galhotra, Romila Pradhan, and Babak Salimi · 2021
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Partial counterfactual identification from observational and experimental data
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Learning generalized gumbel-max causal mechanisms
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Ncore: Neural counterfactual representation learning for combinations of treatments
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Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis
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D’artagnan: Counterfactual video generation
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