Asymptotics for Statistical Treatment Rules

By Keisuke Hirano, J. Porter. 2009

249 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, methodology): “In the econometrics literature, Graham, Imbens and Ridder (2014), Dehejia (2005), Hirano and Porter (2009), Manski (2004), and Bhattacharya and Dupas (2012) estimate parametric or semi-parametric models for optimal policies, relying on regularization for covariate selection in the case of…”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “…in a wide variety application areas (Athey, 2017), ranging from personalized medicine (Ober-meyer and Emanuel, 2016) to oﬄine evaluation of bandits (Dud´ık et al., 2011), and is also a key component of several proposals for learning decision rules (Athey and Wager, 2017; Hirano and Porter, 2009).”
Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “…literature.3 More directly, it is complementary to the small literature on the effect of treatment assignment rules (Manski (2004), Dehejia (2005), Hirano and Porter (2009), 3For recent surveys, see Angrist and Krueger (1999), Heckman, Lalonde, and Smith (2000), and Imbens and Wooldridge (2009).”
Using Experiments to Correct for Selection in Observational Studies (2020, unknown): “…include simple average treatment effects, but more generally also the average effect of a policy that assigns the treatment to individuals in this population on the basis of covariates ( e.g. , Manski [2004], Dehejia [2005], Hirano and Porter [2009], Athey and Wager [2017], Zhou et al. [2018]).”
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].”
Offline Multi-Action Policy Learning: Generalization and Optimization (2018, background): “Both of these assumptions are standard and commonly adopted in both the estimation literature (Rosenbaum and Rubin (1983), Imbens (2004), Imbens and Rubin (2015), Athey et al.”
Statistical decisions under ambiguity (2011, methodology): “…2006) uses it to choose between “selection” and “shrinkage” estimators, DasGupta and Studden (1991) employ it in regression design, Manski (2004), Hirano and Porter (2005), Schlag (2006), and Stoye (2006) apply it to treatment choice in identified models but with finite samples, and Schlag…”
Statistical treatment choice based on asymmetric minimax regret criteria (2009, background): “…asymmetric consideration of Type I regret (due to mistakenly using an inferior new treatment) and Type II regret (due to 1 It should be noted that Hirano and Porter (2009) consider local asymptotic minimax regret criterion, which di¤ers from the "global" minimax regret considered by other…”
Econometric Methods for Program Evaluation (2018, methodology): “…(VanderWeele, 2015; Lok, 2016), dynamic treatment effects and duration models (Abbring and Van den Berg, 2003; Abbring and Heckman, 2007), bounds and partial identification methods (Manski, 2008; Tamer, 2010), and optimal design of policies (Hirano and Porter, 2009; Kitagawa and Tetenov, 2017).”
Minimax regret treatment choice with covariates or with limited validity of experiments (2012, background): “Part (iii) follows from results in both Hirano and Porter (2009) and Tetenov (2009a).”