Abstract
Background: Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable. Objectives: This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods. Design: Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. Subjects: The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely. Conclusions: We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.
Original language | English (US) |
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Pages (from-to) | 405-431 |
Number of pages | 27 |
Journal | Evaluation Review |
Volume | 37 |
Issue number | 5 |
DOIs | |
State | Published - Oct 2014 |
Keywords
- causal inference
- confounder bias
- indirect effects
- mediation
- sensitivity analysis
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- General Social Sciences