Premium
Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach
Author(s) -
Baker Stuart G.
Publication year - 2011
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2010.01451_1.x
Subject(s) - causal inference , multinomial distribution , inference , randomization , outcome (game theory) , biometrics , computer science , estimation , econometrics , statistics , mathematics , randomized controlled trial , medicine , artificial intelligence , mathematical economics , surgery , management , economics
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.