Assessing the Robustness of Mediation Analysis in the Presence of Outliers: A Simulation Study

Oregon Convention Center 

Causal inference is an analytical framework used to identify and understand cause-and-effect relationships between variables in observational studies. In health sciences, where controlled experiments are often difficult or unethical to conduct, causal inference plays a crucial role in drawing conclusions about the impact of various factors on outcomes. Most existing causal inference methods rely on the hypothesis that the distributional properties needed to run the models are not violated. However, outliers may violate the assumptions of statistical models, such as normality or linearity. If causal inference methods depend on these assumptions, the presence of outliers can lead to model misspecification and biased results. Outliers might significantly impact inference, as they have the potential to distort the estimation of causal relationships between variables and influence the sensitivity of causal inference analyses. Thus, in this study, we evaluate the effect of outliers on mediation analysis using an extensive simulation study, demonstrating how they pose challenges by distorting estimates, introducing bias, and affecting the generalizability of results.

Causal inference

Outliers

Robustness 

Section on Statistical Computing