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Evaluating treatment effect heterogeneity widely informs treatment decision making.
The limits of distribution-free conditional predictive inference
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Keisuke Hirano, Guido W Imbens, and Geert Ridder · 2003
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Nicolai Meinshausen and Greg Ridgeway · 2006
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Habiba Djebbari and Jeffrey Smith · 2008
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Identification of treatment effects using control functions in models with continuous, endogenous treatment and heterogeneous effects
Jean-Pierre Florens, James J Heckman, Costas Meghir, and Edward Vytlacil · 2008
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Glenn Shafer and Vladimir Vovk · 2008
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Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education
Jennie E Brand and Yu Xie · 2010
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Hugh A Chipman, Edward I George, and Robert E McCulloch · 2010
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Jennifer L Hill · 2011
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Estimation of heterogeneous treatment effects from randomized experiments, with application to the optimal planning of the get-out-the-vote campaign
Kosuke Imai and Aaron Strauss · 2011
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Vincent Dorie · 2017
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Justin Grimmer, Solomon Messing, and Sean J Westwood · 2017
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Roger Koenker · 2017
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Quasi-oracle estimation of heterogeneous treatment effects
Xinkun Nie and Stefan Wager · 2017
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Dmitry Arkhangelsky, Susan Athey, David A Hirshberg, Guido W Imbens, and Stefan Wager · 2018
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Elizabeth A Stuart, Stephen R Cole, Catherine P Bradshaw, and Philip J Leaf · 2011
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Modeling heterogeneous treatment effects in survey experiments with bayesian additive regression trees
Donald P Green and Holger L Kern · 2012
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Estimating heterogeneous treatment effects with observational data
Yu Xie, Jennie E Brand, and Ben Jann · 2012
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Jing Lei, James Robins, and Larry Wasserman · 2013
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Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality
Thomas S Richardson and James M Robins · 2013
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Improving generalizations from experiments using propensity score subclassification: Assumptions, properties, and contexts
Elizabeth Tipton · 2013
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Eli Ben-Michael, Avi Feller, and Jesse Rothstein · 2018
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Distribution-free predictive inference for regression
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The book of why: the new science of cause and effect
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Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey · 2018
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Susan Athey, Julie Tibshirani, and Stefan Wager · 2019
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The conditional permutation test for independence while controlling for confounders
Thomas B Berrett, Yi Wang, Rina Foygel Barber, and Richard J Samworth · 2019
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Causal processes in psychology are heterogeneous
Niall Bolger, Katherine S Zee, Maya Rossignac-Milon, and Ran R Hassin · 2019
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Assessing treatment effect variation in observational studies: Results from a data challenge
Carlos Carvalho, Avi Feller, Jared Murray, Spencer Woody, and David Yeager · 2019
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Dylan J Foster and Vasilis Syrgkanis · 2019
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gbm: Generalized Boosted Regression Models , 2019
Brandon Greenwell, Bradley Boehmke, Jay Cunningham, and GBM Developers · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
Sören Künzel, Jasjeet Sekhon, Peter Bickel, and Bin Yu · 2019
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Yaniv Romano, Evan Patterson, and Emmanuel Candès · 2019
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Least ambiguous set-valued classifiers with bounded error levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman · 2019
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Use of directed acyclic graphs (dags) in applied health research: review and recommendations
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Evaluation of differences in individual treatment response in schizophrenia spectrum disorders: a meta-analysis
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A national experiment reveals where a growth mindset improves achievement
David S Yeager, Paul Hanselman, Gregory M Walton, Jared S Murray, Robert Crosnoe, Chandra Muller, Elizabeth Tipton, Barbara Schneider, Chris S Hulleman, and Cintia P Hinojosa · 2019
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Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
Richard P Hahn, Jared S Murray, and Carlos M Carvalho · 2020
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Optimal doubly robust estimation of heterogeneous causal effects
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