Methodological and Technical Research Topics | The Methodology Center

Methodological and Technical Research Topics

Inferring Causality in the Presence of a Mediator

Determing causality in the presence of a mediatorPropensity scores are a very useful tool for inferring causality when a mediator is present. When individuals are not randomly assigned to levels of the mediator (which is often the case), propensity scores can be used to account for confounding. 

For example, in an HIV-prevention intervention, subjects receive condom-use training (a treatment), which impacts their belief that they can successfully use condoms (a mediator), which in turn improves their condom use (an outcome). Because the mediator (condom-use self-efficacy) cannot be randomly assigned, the use of propensity scores can enable much more accurate causal inference than the traditional approach of using multivariate regression alone.

 

In the article, "Estimating causal effects in mediation analysis using propensity scores" (2011) Methodology Center Research Associate Donna Coffman proposes and demonstrates that incorporating propensity scores into the traditional approach can remove the bias that occurs when levels of the mediator are not randomly assigned.

 

References & recommended reading

Coffman, D. L., Melde, C., & Esbensen, F. - A. (2014). Gang membership and substance use: Guilt as a gendered vausal pathway. Journal of Experimental Criminology. Advance online publication. doi: 10.1007/s11292-014-9220-9 

Coffman, D. L., & Kugler, K. C. (2012). Causal mediation of a human immunodeficiency virus preventive intervention. Nursing Research, 61(3), 224-230PMCID: PMC2646486

Coffman, D. L., & Zhong, W. (2012). Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychological Methods, 17(4), 642-664doi: 10.1037/a0029311 PMCID: PMC3553264

Coffman, D. L. (2011). Estimating causal effects in mediation analysis using propensity scores. Structural Equation Modeling, 18(3), 357-369. PMCID: PMC3212948 View article


 

Inferring Causality in the Presence of a Moderator

In a recent paper, boredom with leisure activities is hypothesized to influence whether or not teens start smoking (Coffman, Caldwell, & Smith, 2012). The moderator in the model is teen's level of smoking initiation risk. Researchers found that for teens with high levels of risk, leisure boredom did not impact their rates of smoking initiation. However, for teens with low levels of risk, leisure boredom did increase their rates of smoking initiation.

 

References & recommended reading

Coffman, D. L., Melde, C., & Esbensen, F. - A. (2014). Gang membership and substance use: Guilt as a gendered vausal pathway. Journal of Experimental Criminology. Advance online publication. doi: 10.1007/s11292-014-9220-9 

Coffman, D. L., Caldwell, L. L., & Smith, E. A. (2012). Introducing the at-risk average causal effect with application to HealthWise South Africa. Prevention Science, 13, 437-447. PMCID: PMC3405190

Almirall, D., McCaffrey, D. F., Ramchand, R., & Murphy, S. A. (2011). Subgroups analysis when treatment and moderators are time-varying. Prevention Science,doi:10.1007/s11121-011-0208-7 PMCID: PMC3135740   View abstract

Almirall, D., Ten Have, T., & Murphy, S. A. (2010). Structural nested mean models for assessing time-varying effect moderation. Biometrics, 66(1), 131-139. PMCID: PMC2875310  View article

 


 

Inferring Causality in the Presence of Moderated Mediation

Methodology Center researchers have found that the effect of a treatment on an outcome through the mediator can be different for different subgroups of people. For example, in boys, condom-use self-efficacy impacts condom use, but in girls condom-use self-efficacy has no impact.

 

References & recommended reading

Coffman, D. L., & Zhong, W. (2012). Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychological Methods, 17,(4), 342-664. PMCID: PMC3553264 doi: 10.1037/a0029311

 


 

Methods for Estimating Propensity Scores

Our work in this area demonstrates and expands the use of propensity score methods. An article by Debashis Ghosh (2011) shows that certain conditional independence assumptions from dimension-reduction procedures can be used to justify the validity of causal inference modeling. The author studies links between the two fields in this article. The methods are illustrated with simulated data and are applied to a medical study.

 

In the article, "A boosting algorithm for estimating generalized propensity scores with continuous treatments," the authors propose a stopping criterion, the absolute average correlation coefficient, to determine the optimal number of trees (Zhu, Coffman, & Ghosh, in press).   

  

References & recommended reading

Zhu, Y., Coffman, D. L., & Ghosh, D. (in press). A boosting algorithm for estimating generalized propensity scores with continuous treatments. Journal of Causal Inference.

McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., & Burgette, L. F. (2013). A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Statistics in Medicine, 32, 3388-414.

Ghosh, D. (2011). Propensity score modeling in observational studies using dimension reduction methods. Statistics & Probability Letters, 81(7), 813-820. doi:10.1016/j.spl.2011.03.002 PMCID: PMC3099445

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