Statistical treatment choice based on asymmetric minimax regret criteria
By Aleksey Tetenov. 2009
117 citations recorded by the source corpus.
This paper studies the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a finite sample. I evaluate statistical decision rules, which are functions that map sample outcomes into the planner’s treatment choice for the population, based on regret, which is the expected welfare loss due to assigning inferior treatments. 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 superior innovation) and derive exact finite sample solutions to these problems for experiments with normal, Bernoulli and bounded distributions of outcomes. The paper also evaluates the properties of treatment choice and sample size selection based on classical hypothesis tests and power calculations in terms of regret.
Subfields: Economics and Econometrics, General Decision Sciences, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ac79f4553569ef0051d0cf30e163d0cdcadfee24
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Susan Athey
- Stefan Wager
- Jörg Stoye
- J. Porter
- Keisuke Hirano
- Guido W. Imbens
- Debopam Bhattacharya
- M. D. Cattaneo
In General Decision Sciences
In Statistics and Probability
- Susan Athey
- Stefan Wager
- Jörg Stoye
- Guido W. Imbens
- J. Porter
- Keisuke Hirano
- Nathan Kallus
- Mihaela van der Schaar
Citations in the corpus
- Minimax regret treatment choice with covariates or with limited validity of experiments (2012, methodology): “Part (iii) follows from results in both Hirano and Porter (2009) and Tetenov (2009a).”
- Policy Learning With Observational Data (2017, background): “Further results are given in Armstrong and Shen (2013), Bhattacharya and Dupas (2012), Chamberlain (2011), Dehejia (2005), Kasy (2016), Stoye (2012) and Tetenov (2012).”
- Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “Our family of reallocations, which we call correlated matching rules, traces a path from the positive to the negative assortative matching allocations.”
- Impossibility Results for Nondifferentiable Functionals (2012, background): “Recent work on optimal treatment assignment rules by Manski (2004), Dehejia (2005), Stoye (2006), Schlag (2006), Tetenov (2007), and Hirano and Porter (2008) has adopted a decision-theoretic approach for choosing the optimal treatment.”