Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints

By Debopam Bhattacharya, P. Dupas. 2012

173 citations recorded by the source corpus.

Subfields: Pediatrics, Perinatology and Child Health, Safety Research, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pediatrics, Perinatology and Child Health

1. Imran Rasul
2. Susan Athey
3. Stefan Wager

In Safety Research

1. Imran Rasul
2. John A. List
3. Gerard J. van den Berg
4. Susan Athey
5. Stefan Wager
6. Bruno Crépon
7. Daniel John Zizzo
8. Steven D. Levitt

In Statistics and Probability

1. Susan Athey
2. Stefan Wager
3. Gerard J. van den Berg
4. Mihaela van der Schaar
5. John A. List
6. Imran Rasul
7. Bruno Crépon
8. Guido W. Imbens

Citations in the corpus

• Efficient Policy Learning (2017, background): “Second, as discussed in Bhattacharya and Dupas (2012), we sometimes need to work with budget constraints that cap the total fraction of the population that may be treated. Furthermore, in some areas it may be desirable to pre-specify (or bound) treatment assignment rates by subgroup. For example, Kl”
• Field experiments in labor economics (2010, background): “Evidence from non-experimental studies, natural experiments, and field experiments suggests that incentives can improve teacher performance (Lavy 2002, Glewwe et al. 2003, Figlio and Kenny 2006, Muralidharan and Sundararaman 2007, Lavy 2009, Duflo 2009).”
• Understanding and misunderstanding randomized controlled trials (2016, unknown): “subjects (see Manski (2004); Manski and Tetenov (2016); Bhattacharya and Dupas (2012) give an application.”
• 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).”
• Who should be Treated? Empirical Welfare Maximization Methods for Treatment Choice (2015, unknown): “In comparison, nonparametric plug-in treatment rules proposed by Bhattacharya and Dupas (2012) are only required to satisfy the capacity constraint on average over repeated data samples.”
• Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “Such rules would require some modification of the methods considered in this paper (cf. Bhattacharya and Dupas (2012)).”
• Active Labor Market Policies (2016, background): “In the very different case of the distribution of mosquito nets to prevent the spread of malaria, Bhattacharya & Dupas (2012) consider the question of maximizing a utilitarian objective by distributing a given number of nets to a large population.”