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Generalized Additive Models
Trevor Hastie and Robert Tibshirani · 1986
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[bayesian analysis in expert systems]: Comment: Graphical models, causality and intervention
Judea Pearl · 1993
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Nuclear feature extraction for breast tumor diagnosis
W Nick Street, William H Wolberg, and Olvi L Mangasarian · 1993
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Models, reasoning and inference
Judea Pearl et al · 2000
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Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A. Lauffenburger, and Garry P. Nolan · 2005
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2010
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On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, and J Mooij · 2012
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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
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Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness
Sander Greenland and Mohammad Ali Mansournia · 2015
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Estimation of interventional effects of features on prediction
Patrick Blöbaum and Shohei Shimizu · 2017
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Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
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Counterfactual Fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 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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When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
Chris Russell, Matt J Kusner, Joshua Loftus, and Ricardo Silva · 2017
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Modernizing the bradford hill criteria for assessing causal relationships in observational data
Louis Anthony Cox Jr · 2018
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A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner, and Ricardo Silva · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
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Fask with interventional knowledge recovers edges from the sachs model
Joseph Ramsey and Bryan Andrews · 2018
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Fairness in decision-making—the causal explanation formula
Dowhy: An end-to-end library for causal inference
Amit Sharma and Emre Kiciman · 2020
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Disaggregated Interventions to Reduce Inequality
Lucius Bynum, Joshua Loftus, and Julia Stoyanovich · 2021
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An exact and robust conformal inference method for counterfactual and synthetic controls
Victor Chernozhukov, Kaspar Wüthrich, and Yinchu Zhu · 2021
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DARPA’s explainable AI (XAI) program: A retrospective
David Gunning, Eric Vorm, Jennifer Yunyan Wang, and Matt Turek · 2021
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Conformal inference of counterfactuals and individual treatment effects
Lihua Lei and Emmanuel J Candès · 2021
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The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable ai
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Junzhe Zhang and Elias Bareinboim · 2018
Cited alongside, same era.
Path-specific counterfactual fairness
Silvia Chiappa · 2019
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Causal discovery toolbox: Uncovering causal relationships in python
Diviyan Kalainathan, Olivier Goudet, and Ritik Dutta · 2019
Cited alongside, same era.
Making Decisions that Reduce Discriminatory Impacts
Matt Kusner, Chris Russell, Joshua Loftus, and Ricardo Silva · 2019
Cited alongside, same era.
Actionable Recourse in Linear Classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Cited alongside, same era.
Causal interpretations of black-box models
Qingyuan Zhao and Trevor Hastie · 2019
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Limitations of interpretable machine learning methods
T Altmann, J Bodensteiner, C Dankers, T Dassen, N Fritz, S Gruber, et al · 2020
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Donghee Shin · 2021
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A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martín Pereira-Fariña · 2021
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Causal Intersectionality and Fair Ranking
Ke Yang, Joshua R. Loftus, and Julia Stoyanovich · 2021
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Dowhy-gcm: An extension of dowhy for causal inference in graphical causal models
Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, and Dominik Janzing · 2022
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Towards Causal Algorithmic Recourse
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, and Isabel Valera · 2022
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Predicting and explaining employee turnover intention
Matilde Lazzari, Jose M Alvarez, and Salvatore Ruggieri · 2022
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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
Christoph Molnar · 2022
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Conformal sensitivity analysis for individual treatment effects
Mingzhang Yin, Claudia Shi, Yixin Wang, and David M Blei · 2022
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Counterfactuals for the future
Lucius Bynum, Joshua Loftus, and Julia Stoyanovich · 2023
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Sensitivity analysis of individual treatment effects: A robust conformal inference approach
Ying Jin, Zhimei Ren, and Emmanuel J Candès · 2023
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Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V. Carvalho, Eduardo M. Pereira, and Jaime S. Cardoso · 2079
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