Abstract
Recent advances in causal inference have given rise to a general and easy-to-use formula for assessing the extent to which
the effect of one variable on another is mediated by a third. This Mediation Formula is applicable to nonlinear models with
both discrete and continuous variables, and permits the evaluation of path-specific effects with minimal assumptions regarding
the data-generating process. We demonstrate the use of the Mediation Formula in simple examples and illustrate why parametric
methods of analysis yield distorted results, even when parameters are known precisely. We stress the importance of distinguishing
between the necessary and sufficient interpretations of “mediated-effect” and show how to estimate the two components in nonlinear
systems with continuous and categorical variables.
the effect of one variable on another is mediated by a third. This Mediation Formula is applicable to nonlinear models with
both discrete and continuous variables, and permits the evaluation of path-specific effects with minimal assumptions regarding
the data-generating process. We demonstrate the use of the Mediation Formula in simple examples and illustrate why parametric
methods of analysis yield distorted results, even when parameters are known precisely. We stress the importance of distinguishing
between the necessary and sufficient interpretations of “mediated-effect” and show how to estimate the two components in nonlinear
systems with continuous and categorical variables.
- Content Type Journal Article
- Pages 1-11
- DOI 10.1007/s11121-011-0270-1
- Authors
- Judea Pearl, Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095-1596, USA
- Journal Prevention Science
- Online ISSN 1573-6695
- Print ISSN 1389-4986