Statistical treatment rules for heterogeneous populations

By Charles F. Manski. 2003

474 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “…treatment in settings with non-compliance an alternative to making additional assumptions, is to focus on getting ranges of values for the estimand that are consistent with the data in a bounds or partial identification approach in a line of research associated with Manski (1990, 1996, 2003, 2013).”
Efficient Policy Learning (2017, methodology): “6Following standard practice in this literature, we focus on establishing upper bounds rather than exact expressions for R(π) (e.g., Kitagawa and Tetenov, 2015; Manski, 2004; Swaminathan and Joachims, 2015), for the simple reason that exact asymptotics for discrete optimization problems of this type”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “This problem is closely related to, but subtly diﬀerent from the problem of estimating τ ∗ ( · ) under squared-error loss; see Kitagawa and Tetenov (2018), Manski (2004) and Murphy (2005) for a discussion.”
Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints (2012, methodology): “This problem has been analyzed in the literature as a statistical decision problem with finite samples under ambiguity but with no budget constraint (c.f., Dehejia (2001), Manski (2004, 2005), Stoye (2009), Hirano and Porter (2009) and Tetenov (2008)).”
Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “More directly, it is complementary to the small literature on the effect of treatment assignment rules (Manski, 2004; Dehejia, 2004; Hirano and Porter, 2005).”
Adaptive Interventions Treatment Modelling and Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) and Q-Learning (2018, methodology): “Another method of describing optimal treatment is derived from different econometrics and bio-medical literatures is known as welfare contrast [22-27].”
Statistical treatment choice based on asymmetric minimax regret criteria (2009, methodology): “I extend previous work started by Manski (2004) that applied the minimax regret criterion to treatment choice problems by considering decision criteria that asymmetrically treat Type I regret (due to mistakenly choosing an inferior new treatment) and Type II regret (due to mistakenly rejecting a…”
Instrumental Variables: An Econometrician's Perspective (2014, unknown): “There is a growing literature developing methods for establishing confidence intervals for parameters in settings with partial identification taking sampling uncertainty into account; see Imbens and Manski (2004) and Chernozhukov, Hong and Tamer (2007).”
Statistical decisions under ambiguity (2011, background): “A clean example of this is the progression from Manski’s (2000) description of the ambiguity inherent in a decision problem to his use of minimax regret ( Manski 2004, 2005 ) to solve that problem.”
Minimax-regret treatment choice with missing outcome data (2007, methodology): “ Manski (2004) used the Wald framework and, in particular, the Savage”