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This paper proposes a novel framework of aggregated intersection of regression functions, where the target parameter is obtained by averaging the minimum (or maximum) of a collection of regression functions over the covariate space.
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Tsybakov, A. B. (2004) · 2004
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Chernozhukov, V., Hong, H., and Tamer, E. (2007) · 2007
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Beresteanu, A. and Molinari, F. (2008) · 2008
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Partial identification of probability distributions with misclassified data
Molinari, F. (2008) · 2008
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Cilibero, F. and Tamer, E. (2009) · 2009
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Lee, D. (2009) · 2009
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Minimax regret treatment choice with finite samples
Stoye, J. (2009) · 2009
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Zhang, J. L., Rubin, D. B., and Mealli, F. (2009) · 2009
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Fan, Y. and Park, S. S. (2010) · 2010
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Sharp identification regions in models with convex moment predictions
Beresteanu, A., Molchanov, I., and Molinari, F. (2011) · 2011
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Performance guarantees for individualized treatment rules
Qian, M. and Murphy, S. A. (2011) · 2011
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Han, S. (2021) · 2021
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Evidence aggregation for treatment choice
Ishihara, T. and Kitagawa, T. (2021) · 2021
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Partial identification and inference for conditional distributions of treatment effects
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Impossibility results for nondifferentiable functionals
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Semiparametric doubly robust targeted double machine learning: a review
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Discordant relaxations of misspecified models
Li, L., Kédagni, D., and Mourifié, I. (2022) · 2022
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Marginal treatment effects with a misclassified treatment
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Role models and revealed gender-specific costs of stem in an extended roy model of major choice
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