Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program

By Alessandra Mattei, Fan Li, Fabrizia Mealli. 2013

52 citations recorded by the source corpus.

The causal effect of a randomized job training program, the JOBS II study, on trainees' depression is evaluated. Principal stratification is used to deal with noncompliance to the assigned treatment. Due to the latent nature of the principal strata, strong structural assumptions are often invoked to identify principal causal effects. Alternatively, distributional assumptions may be invoked using a model-based approach. These often lead to weakly identified models with substantial regions of flatness in the posterior distribution of the causal effects. Information on multiple outcomes is routinely collected in practice, but is rarely used to improve inference. This article develops a Bayesian approach to exploit multivariate outcomes to sharpen inferences in weakly identified principal stratification models. We show that inference for the causal effect on depression is significantly improved by using the re-employment status as a secondary outcome in the JOBS II study. Simulation studies are also performed to illustrate the potential gains in the estimation of principal causal effects from jointly modeling more than one outcome. This approach can also be used to assess plausibility of structural assumptions and sensitivity to deviations from these structural assumptions. Two model checking procedures via posterior predictive checks are also discussed.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/67548529695ba7f337d5c8238d0eb037d1e64222

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In Statistics and Probability

1. Peng Ding
2. Zhi Geng
3. Jiannan Lu
4. Zhichao Jiang
5. Xinran Li
6. Kevin Guo
7. Guillaume W. Basse

Citations in the corpus

• Principal causal effect identification and surrogate end point evaluation by multiple trials (2015, background): “…Yan et al. (2011), Mealli and Pacini (2013) and Yang and Small (2015) achieved partial and point identification of principal causal effects by exploiting covariates or secondary outcomes, and Mattei et al. (2013) improved Bayesian inference for principal causal effects by using multiple outcomes.”
• Principal stratification analysis using principal scores (2016, background): “In observational studies, causal effects can be estimated by inverse propensity score weighting (Rosenbaum and Rubin, 1983a), which may be numerically unstable and have poor finite sample properties. Our estimators, weighted by probabilities themselves, do not suffer from these problems. Researchers”