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The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed intervention.
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Estimation and confidence regions for parameter sets in econometric models 1
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Sergio Firpo and Geert Ridder · 2008
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Elie Tamer · 2010
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Estimating treatment effect heterogeneity in randomized program evaluation
Kosuke Imai and Marc Ratkovic · 2013
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A solution to the ecological inference problem
Gary King · 2013
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Fairness under unawareness: Assessing disparity when protected class is unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell · 2019
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Decomposing treatment effect variation
Peng Ding, Avi Feller, and Luke Miratrix · 2019
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Assessing disparate impacts of personalized interventions: Identifiability and bounds
Nathan Kallus and Angela Zhou · 2019
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Interval estimation of individual-level causal effects under unobserved confounding
Nathan Kallus, Xiaojie Mao, and Angela Zhou · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu · 2019
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Private and public provision of counseling to job seekers: Evidence from a large controlled experiment
Luc Behaghel, Bruno Crépon, and Marc Gurgand · 2014
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Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
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Recursive partitioning for heterogeneous causal effects
Susan Athey and Guido Imbens · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables
Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2017
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Debiased machine learning of set-identified linear models
Vira Semenova · 2017
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Samir Passi and Solon Barocas · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Fairlearn: A toolkit for assessing and improving fairness in ai
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Fairness evaluation in presence of biased noisy labels
Riccardo Fogliato, Alexandra Chouldechova, and Max G’Sell · 2020
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Optimal doubly robust estimation of heterogeneous causal effects
Edward H Kennedy · 2020
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Microeconometrics with partial identification
Francesca Molinari · 2020
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Sensitivity analysis via the proportion of unmeasured confounding
Matteo Bonvini and Edward H Kennedy · 2021
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Omitted variable bias in machine learned causal models
Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, and Vasilis Syrgkanis · 2021
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Characterizing fairness over the set of good models under selective labels
Amanda Coston, Ashesh Rambachan, and Alexandra Chouldechova · 2021
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Doubly-valid/doubly-sharp sensitivity analysis for causal inference with unmeasured confounding
Jacob Dorn, Kevin Guo, and Nathan Kallus · 2021
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Characterizing intersectional group fairness with worst-case comparisons
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Assessing algorithmic fairness with unobserved protected class using data combination
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Algorithmic fairness: Choices, assumptions, and definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum · 2021
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Quasi-oracle estimation of heterogeneous treatment effects
Xinkun Nie and Stefan Wager · 2021
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Treatment effect risk: Bounds and inference
Nathan Kallus · 2022
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