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We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges.
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Random forests
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Observational Studies
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Mostly harmless econometrics: An empiricist’s companion
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Estimating treatment effect heterogeneity in randomized program evaluation
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A. Belloni, V. Chernozhukov, and C. Hansen · 2014
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S. Künzel, J. Sekhon, P. Bickel, and B. Yu · 2017
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R: A Language and Environment for Statistical Computing
R Core Team · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
U. Shalit, F. D. Johansson, and D. Sontag · 2017
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Selective inference for effect modification via the lasso
Q. Zhao, D. S. Small, and A. Ertefaie · 2017
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Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school
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V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions
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Is the national study of learning mindsets nationally-representative?
M. Gopalan and E. Tipton · 2018
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grf: Generalized Random Forests (Beta) , 2018
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Estimation and inference of heterogeneous treatment effects using random forests
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Generalized random forests
S. Athey, J. Tibshirani, and S. Wager · 2019
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