
Home
Studium
Workshops / Konferenzen
Videos
Forschung
Dienstleistungen
Team
Impressum / Kontakt
Besucher seit 27.02.2014: ganze Website: 262056
aktuelle Seite: 380

Kurse: Kursinformationen
Symposium on Causality 2012
Kursleitung: Prof. Dr. Rolf Steyer et al. Sommersemester 2012, Konferenz, Kurslänge: 14.25 Stunden, Sprache: Englisch, Thema: Analysis of causal effects
Symposium "Causality in Educational Research"
From July 24 to 26, 2012 was held a conference at the "Altes Schloss Dornburg" near Jena.
Aim of the symposium
The symposium is intended to sharpen our ideas and instruments for the analysis of conditional and direct treatment effects. Why does this aim seem to be warranted? In the last decades, analyses of causal effects have mainly focussed on average total effects of treatments. This is appropriate if we are interested in the overall or total effectiveness of treatments in a population of subjects. However, conditional and direct treatment (intervention, exposition) effects are more informative and scientifically more interesting (MacKinnon, 2008).
With conditional total effects we consider the effects of a treatment on an outcome variable given a value of one or more covariates. Such a covariate can be sex, diagnostic group, severity of symptoms before treatment, motivation for treatment, or a multivariate variable consisting of several of such onedimensional variables. Such conditional total effects can differ from each other between different values of the covariates. Hence, with conditional total effects we can quantify the total treatment effects for different (groups of) subjects. How big are the total effects for which kind of subjects? Note that total treatment effects may be positive for some and negative for others. Hence, in clinical research, conditional total effects deal with differential indication of a treatment.
Average and conditional total effects deal with the total effects of a treatment. In contrast, with direct effects we ask for the effects of a treatment that are not transmitted by intermediate variables that, in the process considered, occur up to a specified time point. Controlling for all pretreatment variables and all variables in between treatment and a specified time point of the process, is there still an effect of the treatment? How big are these direct effects on average? How do they differ for different values of the variables controlled? Hence, now we ask: How big are the direct effects for which kinds of subjects? In educational research we may ask if the effects of a teaching method are transmitted to the outcome variable by raising the motivation to learn and by increasing time spent on learning, or if the total effect observed is not mediated through these and other variables occurring in the process up to the time point to which time spent on learning refers.
Asking for direct causal effects of a treatment has gained increasing interest in the last years because it has been shown that the wellknown path analysis procedures for the analysis of direct and indirect effects often lead to systematically wrong results even in the randomized experiment. This is due to the fact that independence of treatment and covariates induced by randomization is cancelled as soon as we condition on an intermediate variable that is affected by treatment and is correlated with a pretreatment variable. In this case, the partial correlation between the treatment variable and the covariate, controlling for the intermediate variable, is not zero. For example, if the intermediate motivation is affected by treatment and it depends on pretreatment motivation, then a high score on intermediate motivation goes along with a high score on treatment (e. g., 1) and a high score on pretreatment motivation, whereas a low score on intermediate motivation goes along with a low score on treatment (e. g., 0) and a low score on pretreatment motivation. Independence of the treatment variable and all pretreatment variables (induced by randomization) implies unbiasedness of mean differences of the outcome variable between treatment groups, but it does not imply unbiasedness of direct effects if we only include treatment, intermediate, and outcome variables in our analysis.
References:
 Baron, R. M., & Kenny, D. A. (1986). The moderatormediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51(6), 11731182.
 MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum.
Organizational structure
The structure of the conference was closely followed the "Symposium on Causality 2010". There were 5 focus presentations by leading proponents in different fields of causality research. Each focus presentation was discussed and supplemented by two invited discussants, followed by an open discussion among all participants. There was also be room for participants to present their own research in short presentations.
For further information, please visit the website of the Symposium on Causality 2012.
Um die herunterladbaren Videos anzuschauen, benötigen Sie den VLC media player, den Sie hier herunterladen können. Die Videos werden bereitgestellt über die Digitale Bibliothek Thüringen (dbt). Die grün verlinkten Videos und Materialien sind kostenfrei abrufbar. Klicken Sie auf den grünen Link, um das Video zu sehen oder die Datei herunterzuladen! Um alle Materialien und Videos abrufen zu können, müssen Sie sich einloggen. Videos
Material related to the contributions
Focus presentations
Philip Dawid  "A probabilistic approach to direct and indirect effects"
 Didelez, V., Dawid, A. P. and Geneletti, S. (2006). Direct and indirect effects of sequential treatments. In Proc. 22nd Annual Conference on Uncertainty in Artical Intelligence (R. Dechter and T. S. Richardson, Eds.). AUAI Press, Arlington, Virginia, 138146.
 Geneletti, S. (2007). Identifying direct and indirect effects in a noncounterfactual framework. J. R. Statist. Soc. B 69, Part 2, 199215.
Kosuke Imai  "The potential outcome approach to direct effects: Definition, identification, and sensitivity analysis"
David MacKinnon  "Introduction to Mediation Analysis: Importance, Applications, and Examples"
Rolf Steyer  "The definition of total, direct and indirect effects and their identification"
Johannes Textor  "Graphical Approaches to Covariate Selection for Direct and Partial Causal Effects"
Felix Thoemmes  "Covariate selection for total and direct effects: A comparative view"
Short contributions
Sonja Hahn
Martin Huber
Ana Kolar
Boris Mayer
MarieAnn Sengewald


