Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach
Article first published online: 16 JUN 2010
© 2010, The International Biometric Society No claim to original US government works
Volume 67, Issue 1, pages 319–323, March 2011
How to Cite
Baker, S. G. (2011), Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach. Biometrics, 67: 319–323. doi: 10.1111/j.1541-0420.2010.01451_1.x
- Issue published online: 14 MAR 2011
- Article first published online: 16 JUN 2010
- Received August 2009. Revised October 2009. Accepted October 2009.
- All-or-none compliance;
- Causal effect;
- Multinomial outcomes;
- Perfect fit;
- Principal stratification;
- Randomized trials
Summary Recently, Cheng (2009, Biometrics 65, 96–103) proposed a model for the causal effect of receiving treatment when there is all-or-none compliance in one randomization group, with maximum likelihood estimation based on convex programming. We discuss an alternative approach that involves a model for all-or-none compliance in two randomization groups and estimation via a perfect fit or an expectation–maximization algorithm for count data. We believe this approach is easier to implement, which would facilitate the reproduction of calculations.