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Estimating personalized effects of treatments is a complex, yet pervasive problem.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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Semiparametric efficiency in multivariate regression models with missing data
James M Robins and Andrea Rotnitzky · 1995
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Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin · 2005
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The costs of low birth weight
Douglas Almond, Kenneth Y Chay, and David S Lee · 2005
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Factors predictive of response to hormone therapy in breast cancer
Francesca Rastelli and Sergio Crispino · 2008
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Bag of words data set
David Newman · 2008
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Polynomial calculation of the Shapley value based on sampling
Javier Castro, Daniel Gómez, and Juan Tejada · 2009
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Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
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Subgroup identification from randomized clinical trial data
J. Foster, J. Taylor, and S. Ruberg · 2011
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Estimating treatment effect heterogeneity in randomized program evaluation
Kosuke Imai, Marc Ratkovic, et al · 2013
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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The cancer genome atlas pan-cancer analysis project
John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Shaw, Brad A Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, and Joshua M Stuart · 2013
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Biomarker: predictive or prognostic?
Karla V Ballman · 2015
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Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Recursive partitioning for heterogeneous causal effects
Susan Athey and Guido Imbens · 2016
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"Why should i trust you?" Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus Robert Müller, and Wojciech Samek · 2016
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Bayesian inference of individualized treatment effects using multi-task gaussian processes
Ahmed M Alaa and Mihaela van der Schaar · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Learning cost-effective and interpretable treatment regimes
Himabindu Lakkaraju and Cynthia Rudin · 2017
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Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Understanding Black-box Predictions via Influence Functions
Pang Wei Koh and Percy Liang · 2017
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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Erico Tjoa and Cuntai Guan · 2020
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Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
Arun Das and Paul Rad · 2020
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Adaptive hyper-box matching for interpretable individualized treatment effect estimation
Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky · 2020
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Causal rule ensemble: Interpretable inference of heterogeneous treatment effects
Kwonsang Lee, Falco J Bargagli-Stoffi, and Francesca Dominici · 2020
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Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines
Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E Berger, Subhro Das, and Kush R Varshney · 2020
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Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2018
Cited alongside, same era.
Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
Ahmed Alaa and Mihaela van der Schaar · 2018
Cited alongside, same era.
Regularization and confounding in linear regression for treatment effect estimation
P Richard Hahn, Carlos M Carvalho, David Puelz, Jingyu He, et al · 2018
Cited alongside, same era.
Distinguishing prognostic and predictive biomarkers: an information theoretic approach
Konstantinos Sechidis, Konstantinos Papangelou, Paul D Metcalfe, David Svensson, James Weatherall, and Gavin Brown · 2018
Cited alongside, same era.
Metalearners for estimating heterogeneous treatment effects using machine learning
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu · 2019
Cited alongside, same era.
Generalized random forests
Susan Athey, Julie Tibshirani, Stefan Wager, et al · 2019
Cited alongside, same era.
From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Ioana Bica, Ahmed M Alaa, Craig Lambert, and Mihaela van der Schaar · 2021
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Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms
Alicia Curth and Mihaela van der Schaar · 2021
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Counterfactual representation learning with balancing weights
Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, and Lawrence Carin Duke · 2021
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On inductive biases for heterogeneous treatment effect estimation
Alicia Curth and Mihaela van der Schaar · 2021
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Really doing great at estimating cate? a critical look at ml benchmarking practices in treatment effect estimation
Alicia Curth, David Svensson, Jim Weatherall, and Mihaela van der Schaar · 2021
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On discovering treatment-effect modifiers using virtual twins and causal forest ml in the presence of prognostic biomarkers
Erik Hermansson and David Svensson · 2021
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A causal fused lasso for interpretable heterogeneous treatment effects estimation
Oscar Hernan Madrid Padilla, Peng Ding, Yanzhen Chen, and Gabriel Ruiz · 2021
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Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, and Eytan Bakshy · 2021
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Comparison of feature importance measures as explanations for classification models
Mirka Saarela and Susanne Jauhiainen · 2021
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Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, and Su-In Lee · 2021
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Explaining Time Series Predictions with Dynamic Masks
Jonathan Crabbé and Mihaela van der Schaar · 2021
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Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, and Mihaela van der Schaar · 2021
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Covariate-balancing-aware interpretable deep learning models for treatment effect estimation
Kan Chen, Qishuo Yin, and Qi Long · 2022
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