Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign

By Kosuke Imai, Aaron Strauss. 2011

138 citations recorded by the source corpus.

Subfields: Political Science and International Relations, 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 Political Science and International Relations

1. Sendhil Mullainathan
2. Jann Spiess
3. Elias Dinas

In Statistics and Probability

1. Rafael Valle
2. Sendhil Mullainathan
3. Jann Spiess
4. Jason Poulos
5. Susan Athey
6. Perry Sadorsky
7. Christian Hansen
8. Markku V. J. Maula

Citations in the corpus

• Missing Data Imputation for Supervised Learning (2016, background): “Methodological applications of supervised learning include the estimation of heterogeneous treatment effects (Imai and Strauss, 2011; Green and Kern, 2012; Imai et al., 2013; Grimmer et al., 2014), text analysis (Quinn et al.”
• Machine Learning: An Applied Econometric Approach (2017, methodology): “treatment effects that are estimated using decision trees, as previously suggested by Imai and Strauss (2011).”