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Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit.
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A. Charnes and W. W. Cooper · 1962
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D. B. Rubin · 1980
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Identification of causal effects using instrumental variables
J. D. Angrist, G. W. Imbens, and D. B. Rubin · 1996
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Bounds on treatment effects from studies with imperfect compliance
A. Balke and J. Pearl · 1997
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Monotone treatment response
C. F. Manski · 1997
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Econometric evaluation of labour market policies , chapter EvaluatingProfiling as a Means of Allocating Government Service, pages 59–84
M. Berger, D. A. Black, and J. A. Smith · 2000
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Partial identification of probability distributions
C. F. Manski · 2003
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Social Choice with Partial Knoweldge of Treatment Response
C. Manski · 2005
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Targeting and universalism in poverty reduction
T. Mkandawire · 2005
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Targeting labour market programmes: results from a randomized experiment
S. Behncke, M. Frölich, and M. Lechner · 2007
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Bayesian nonparametric modeling for causal inference
J. L. Hill · 2011
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Partial identification using random set theory
A. Beresteanu, I. Molchanov, and F. Molinari · 2012
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Inferring welfare maximizing treatment assignment under budget constraints
D. Bhattacharya and P. Dupas · 2012
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The tay triage tool: A tool to identify homeless transition age youth most in need of permanent supportive housing
E. Rice · 2013
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Big data’s disparate impact
S. Barocas and A. Selbst · 2014
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Private and public provision of counseling to job seekers: Evidence from a large controlled experiment
L. Behaghel, B. Crépon, and M. Gurgand · 2014
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Doubly robust policy evaluation and optimization
M. Dudik, D. Erhan, J. Langford, and L. Li · 2014
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A multifaceted program causes lasting progress for the very poor: Evidence from six countries
A. Banerjee, E. Duflo, N. Goldberg, D. Karlan, R. Osei, W. Parienté, J. Shapiro, B. Thuysbaert, and C. Udry · 2015
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Causal Inference for Statistics, Social, and Biomedical Sciences
G. Imbens and D. Rubin · 2015
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Empirical welfare maximization
T. Kitagawa and A. Tetenov · 2015
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Convex analysis
R. T. Rockafellar · 2015
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Machine bias
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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A poor means test? econometric targeting in africa
C. Brown, M. Ravallion, and D. van de Walle · 2016
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Predictive analytics for city agencies: Lessons from children’s services
R. Shroff · 2017
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Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2018
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Why is my classifier discriminatory?
I. Chen, F. Johansson, and D. Sontag · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
S. Corbett-Davies and S. Goel · 2018
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Automating inequality: How high-tech tools profile, police, and punish the poor
V. Eubanks · 2018
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Fairness behind a veil of ignorance: A welfare analysis for automated decision making
H. Heidari, C. Ferrari, K. Gummadi, and A. Krause · 2018
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
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Active labor market policies
B. Crepon and G. J. van den Berg · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, N. Srebro, et al · 2016
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Retooling poverty targeting using out-of-sample validation and machine learning
L. McBride and A. Nichols · 2016
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Beyond prediction: Using big data for policy problems
S. Athey · 2017
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Interventions over predictions: Reframing the ethical debate for actuarial risk assessment
C. Barabas, K. Dinakar, J. Ito, M. Virza, and J. Zittrain · 2017
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Algorithmic decision making in the presence of unmeasured confounding
J. Jung, R. Shroff, A. Feller, and S. Goel · 2018
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Residual unfairness in fair machine learning from prejudiced data
N. Kallus and A. Zhou · 2018
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Delayed impact of fair machine learning
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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
S. Mitchell, E. Potash, and S. Barocas · 2018
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Generalized random forests
S. Athey, J. Tibshirani, S. Wager, et al · 2019
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Fair classification and social welfare
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Classifying treatment responders under causal effect monotonicity
N. Kallus · 2019
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The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric
N. Kallus and A. Zhou · 2019
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Allocating interventions based on predicted outcomes: A case study on homelessness services
A. Kube and S. Das · 2019
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Fairness through causal awareness: Learning latent-variable models for biased data
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