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Causal Inference on Total, Direct, and Indirect Effects

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Encyclopedia of Quality of Life and Well-Being Research

Synonyms

Theory of causal effects (TCEs)

Definition

The theory of causal effects (TCEs) is a mathematical theory providing a methodological foundation for design and analysis of experiments and quasi-experiments. TCE consists of two parts. In the first part, total, direct, and indirect effects are defined, the second part deals with causal inference, i.e., in the second part, it is shown how causal effects are identified by estimable quantities. In each part, there are two levels, a disaggregated and a reaggregated one.

In the definition part of TCE, the disaggregated level is called the atomic level. In this part, we translate J. St. Mill’s ceteris paribus clause into probabilistic concepts. For this purpose, we introduce temporal order between events and/or random variables using the concept of a filtration. Defining an atomic total-effect variable, we isolate the effects of \( X \) on \( Y \), controlling for all variables that are prior or simultaneous to \( X \), while ignoring...

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References

  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

    Google Scholar 

  • Bauer, H. (1996). Probability theory. Berlin/Germany/New York: de Gruyter.

    Google Scholar 

  • Bauer, H. (2001). Measure and integration theory. Berlin/Germany/New York: de Gruyter.

    Google Scholar 

  • Bollen, K. A. (1987). Total, direct, and indirect effects in structural equation models. Sociological Methodology, 17, 37–69.

    Google Scholar 

  • Geneletti, S., & Dawid, A. P. (2011). Defining and identifying the effect of treatment on the treated. In Causality in the sciences (pp. 728–749). Oxford: Oxford University Press.

    Google Scholar 

  • Heckman, J. J., & Robb, R. (1985). Alternative methods for evaluating the impact of interventions – An overview. Journal of Econometrics, 30(1–2), 239–267. Available from ISI: A1985AZA7200012.

    Google Scholar 

  • Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equation models. Sociological Methodology, 18, 449–484.

    Google Scholar 

  • Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5(5), 602–619.

    Google Scholar 

  • Klenke, A. (2008). Probability theory – A comprehensive course. London: Springer.

    Google Scholar 

  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum.

    Google Scholar 

  • Mayer, A., Thoemmes, F., Rose, N., Steyer, R., & West, S. G. (2013). Theory and analysis of total, direct and indirect causal effects. Submitted

    Google Scholar 

  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference. Methods and principles for social research. New York: Cambridge University Press.

    Google Scholar 

  • Øksendal, B. (2007). Stochastic differential equations: An introduction with applications (6th ed.). Berlin, Germany: Springer.

    Google Scholar 

  • Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227.

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Google Scholar 

  • Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331.

    Google Scholar 

  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi experimental designs for generalized causal inference. Boston: Houghton Mifflin.

    Google Scholar 

  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.

    Google Scholar 

  • Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT.

    Google Scholar 

  • Splawa-Neyman, J. (1923/1990). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science, 5, 465–480. (Reprinted from roczniki nauk rolniczych tom10, pp. 1–51, 1923)

    Google Scholar 

  • Steyer, R., Nagel, W., Partchev, I., & Mayer, A. (in preparation). Probability and conditional expectation. New York: Springer.

    Google Scholar 

  • Steyer, R., & Partchev, I. (2008). EffectLite for Mplus: A program for the uni-and multivariate analysis of unconditional, conditional and average mean differences between groups [Computer software and manual]. Retrieved May 5, 2008, from www.statlite.com

  • Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. Probability and causality. Heidelberg, Germany: Springer. In preparation.

    Google Scholar 

  • Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.

    Google Scholar 

  • Wright, S. (1923). The theory of path coefficients: A reply to Niles’s criticism. Genetics, 8, 239–255.

    Google Scholar 

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Correspondence to Rolf Steyer .

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Steyer, R., Mayer, A., Fiege, C. (2014). Causal Inference on Total, Direct, and Indirect Effects. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_295

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  • DOI: https://doi.org/10.1007/978-94-007-0753-5_295

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