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Center for Causal Inference

New Publication on Bayesian Marginal Structural Models

New Publication on Bayesian Marginal Structural Models

Jun 26, 2016

A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.

Roy J, Lum KJ, Daniels MJ.

Marginal structural models (MSMs) are a general class of causal models for specifying the average effect of treatment on an outcome. These models can accommodate discrete or continuous treatments, as well as treatment effect heterogeneity (causal effect modification). The literature on estimation of MSM parameters has been dominated by semiparametric estimation methods, such as inverse probability of treatment weighted (IPTW). Likelihood-based methods have received little development, probably in part due to the need to integrate out confounders from the likelihood and due to reluctance to make parametric modeling assumptions. In this article we develop a fully Bayesian MSM for continuous and survival outcomes. In particular, we take a Bayesian nonparametric (BNP) approach, using a combination of a dependent Dirichlet process and Gaussian process to model the observed data. The BNP approach, like semiparametric methods such as IPTW, does not require specifying a parametric outcome distribution. Moreover, by using a likelihood-based method, there are potential gains in efficiency over semiparametric methods. An additional advantage of taking a fully Bayesian approach is the ability to account for uncertainty in our (uncheckable) identifying assumption. To this end, we propose informative prior distributions that can be used to capture uncertainty about the identifying "no unmeasured confounders" assumption. Thus, posterior inference about the causal effect parameters can reflect the degree of uncertainty about this assumption. The performance of the methodology is evaluated in several simulation studies. The results show substantial efficiency gains over semiparametric methods, and very little efficiency loss over correctly specified maximum likelihood estimates. The method is also applied to data from a study on neurocognitive performance in HIV-infected women and a study of the comparative effectiveness of antihypertensive drug classes.


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About CCI

The Center for Causal Inference (CCI) is a research center that is operating under a partnership between Penn’s Center for Clinical Epidemiology and Biostatistics (CCEB), the Department of Biostatistics and Epidemiology, Rutgers School of Public Health, and Penn’s Wharton School. The mission of the CCI is to be a leading center for research and training in the development and application of causal inference theory and methods.

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