Doubly Robust Estimation of Causal Effects
University of North Carolina at Chapel Hill · North Carolina State University
Abstract
Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of…
Citation impact
- FWCI
- 7.82
- Percentile
- 100%
- References
- 34
Authors
6- MJMichele Jönsson FunkCorresponding
University of North Carolina at Chapel Hill
- DWDaniel Westreich
University of North Carolina at Chapel Hill
- CWChris Wiesen
University of North Carolina at Chapel Hill
- TSTil Stürmer
University of North Carolina at Chapel Hill
- MAM. Alan Brookhart
University of North Carolina at Chapel Hill
Topics & keywords
- Estimator
- Propensity score matching
- Statistics
- Robust statistics
- Estimation
- Computer science
- Robust regression
- Causal inference