### Abstract

Mediation analysis, or more generally models with direct and indirect effects, are commonly used in the behavioral sciences. As we show in our illustrative example, traditional methods of mediation analysis that omit confounding variables can lead to systematically biased direct and indirect effects, even in the context of a randomized experiment. Therefore, several definitions of causal effects in mediation models have been presented in the literature (Baron & Kenny, 1986; Imai, Keele, & Tingley, 2010; Pearl, 2012). We illustrate the stochastic theory of causal effects as an alternative foundation of causal mediation analysis based on probability theory. In this theory we define total, direct, and indirect effects and show how they can be identified in the context of our illustrative example. A particular strength of the stochastic theory of causal effects are the causality conditions that imply causal unbiasedness of effect estimates. The causality conditions have empirically testable implications and can be used for covariate selection. In the discussion, we highlight some similarities and differences of the stochastic theory of causal effects with other theories of causal effects.

Original language | English (US) |
---|---|

Pages (from-to) | 425-442 |

Number of pages | 18 |

Journal | Multivariate Behavioral Research |

Volume | 49 |

Issue number | 5 |

DOIs | |

State | Published - Sep 1 2014 |

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### ASJC Scopus subject areas

- Experimental and Cognitive Psychology
- Statistics and Probability
- Arts and Humanities (miscellaneous)

### Cite this

*Multivariate Behavioral Research*,

*49*(5), 425-442. https://doi.org/10.1080/00273171.2014.931797

**Theory and Analysis of Total, Direct, and Indirect Causal Effects.** / Mayer, Axel; Thoemmes, Felix; Rose, Norman; Steyer, Rolf; West, Stephen.

Research output: Contribution to journal › Article

*Multivariate Behavioral Research*, vol. 49, no. 5, pp. 425-442. https://doi.org/10.1080/00273171.2014.931797

}

TY - JOUR

T1 - Theory and Analysis of Total, Direct, and Indirect Causal Effects

AU - Mayer, Axel

AU - Thoemmes, Felix

AU - Rose, Norman

AU - Steyer, Rolf

AU - West, Stephen

PY - 2014/9/1

Y1 - 2014/9/1

N2 - Mediation analysis, or more generally models with direct and indirect effects, are commonly used in the behavioral sciences. As we show in our illustrative example, traditional methods of mediation analysis that omit confounding variables can lead to systematically biased direct and indirect effects, even in the context of a randomized experiment. Therefore, several definitions of causal effects in mediation models have been presented in the literature (Baron & Kenny, 1986; Imai, Keele, & Tingley, 2010; Pearl, 2012). We illustrate the stochastic theory of causal effects as an alternative foundation of causal mediation analysis based on probability theory. In this theory we define total, direct, and indirect effects and show how they can be identified in the context of our illustrative example. A particular strength of the stochastic theory of causal effects are the causality conditions that imply causal unbiasedness of effect estimates. The causality conditions have empirically testable implications and can be used for covariate selection. In the discussion, we highlight some similarities and differences of the stochastic theory of causal effects with other theories of causal effects.

AB - Mediation analysis, or more generally models with direct and indirect effects, are commonly used in the behavioral sciences. As we show in our illustrative example, traditional methods of mediation analysis that omit confounding variables can lead to systematically biased direct and indirect effects, even in the context of a randomized experiment. Therefore, several definitions of causal effects in mediation models have been presented in the literature (Baron & Kenny, 1986; Imai, Keele, & Tingley, 2010; Pearl, 2012). We illustrate the stochastic theory of causal effects as an alternative foundation of causal mediation analysis based on probability theory. In this theory we define total, direct, and indirect effects and show how they can be identified in the context of our illustrative example. A particular strength of the stochastic theory of causal effects are the causality conditions that imply causal unbiasedness of effect estimates. The causality conditions have empirically testable implications and can be used for covariate selection. In the discussion, we highlight some similarities and differences of the stochastic theory of causal effects with other theories of causal effects.

UR - http://www.scopus.com/inward/record.url?scp=84907468729&partnerID=8YFLogxK

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U2 - 10.1080/00273171.2014.931797

DO - 10.1080/00273171.2014.931797

M3 - Article

AN - SCOPUS:84907468729

VL - 49

SP - 425

EP - 442

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

IS - 5

ER -