Doubly Robust Estimation of Causal Effects with Multivalued Treatments: An Application to the Returns to Schooling

2014 ◽  
Vol 30 (5) ◽  
pp. 763-786 ◽  
Author(s):  
S. Derya Uysal
Keyword(s):  
Robust Estimation ◽  
Causal Effects ◽  
Returns To Schooling ◽  
Doubly Robust Estimation ◽  
Doubly Robust

scholarly journals Covariate selection with group lasso and doubly robust estimation of causal effects

Biometrics ◽  
2017 ◽  
Vol 74 (1) ◽  
pp. 8-17 ◽  
Author(s):  
Brandon Koch ◽  
David M. Vock ◽  
Julian Wolfson
Keyword(s):  
Robust Estimation ◽  
Group Lasso ◽  
Causal Effects ◽  
Covariate Selection ◽  
Doubly Robust Estimation ◽  
Doubly Robust

scholarly journals Doubly Robust Estimation of Causal Effects

2011 ◽  
Vol 173 (7) ◽  
pp. 761-767 ◽  
Author(s):  
Michele Jonsson Funk ◽  
Daniel Westreich ◽  
Chris Wiesen ◽  
Til Stürmer ◽  
M. Alan Brookhart ◽  
...  
Keyword(s):  
Robust Estimation ◽  
Causal Effects ◽  
Doubly Robust Estimation ◽  
Doubly Robust

scholarly journals Machine learning outcome regression improves doubly robust estimation of average causal effects

2020 ◽  
Vol 29 (9) ◽  
pp. 1120-1133
Author(s):  
Byeong Yeob Choi ◽  
Chen‐Pin Wang ◽  
Jonathan Gelfond
Keyword(s):  
Machine Learning ◽  
Robust Estimation ◽  
Learning Outcome ◽  
Causal Effects ◽  
Doubly Robust Estimation ◽  
Doubly Robust

scholarly journals Adjustment for Missing Data in Complex Surveys Using Doubly Robust Estimation

Epidemiology ◽  
2010 ◽  
Vol 21 (6) ◽  
pp. 863-871 ◽  
Author(s):  
Kathleen E. Wirth ◽  
Eric J. Tchetgen Tchetgen ◽  
Megan Murray
Keyword(s):  
Missing Data ◽  
Robust Estimation ◽  
Complex Surveys ◽  
Doubly Robust Estimation ◽  
Doubly Robust

Comparing Alternatives for Estimation from Nonprobability Samples

2019 ◽  
Vol 8 (2) ◽  
pp. 231-263 ◽  
Author(s):  
Richard Valliant
Keyword(s):  
Robust Estimation ◽  
Population Control ◽  
Simulation Studies ◽  
Data Set ◽  
External Data ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
External Population ◽  
Selection Probabilities ◽  
As If

Abstract Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly robust estimation. In the first, the sample is treated as if it were obtained via a probability mechanism, but unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population. In the superpopulation approach, observed values of analysis variables are treated as if they had been generated by some model. The model is estimated from the sample and, along with external population control data, is used to project the sample to the population. The specific techniques are the same or similar to ones commonly employed for estimation from probability samples and include binary regression, regression trees, and calibration. When quasi-randomization and superpopulation modeling are combined, this is referred to as doubly robust estimation. This article reviews some of the estimation options and compares them in a series of simulation studies.

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