This course combines the theory of individual and average causal effects in the sense of J. Neyman and D. B. Rubin with analysis techniques of structural equation modelling. All designs and models for the analysis are developed for the purpose to learn about individual, conditional and/or average causal effects. Unlike other courses on the analysis of treatment effects, it uses structural equation modelling (with or without latent variables) instead of analysis of variance techniques, the General Linear Model or related techniques. As will be shown, this will enable us to learn not only about average and conditional effects, but, in specific models, also about individual causal effects.
This course is a synthesis of different traditions in methodology: Rubin's approach to causality, the Campbellian tradition of quasi-experimentation and internal validity, and structural equation modeling, especially latent state-trait modeling, latent change modeling and latent growth curve modeling.
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Individual and Average Causal Effects (Theory)
Motivation: The Simpson paradox
Basic concepts: Individual and average causal effects
Fixed and random effects regression
Generalization to more than two treatment conditions
Identifying the average causal effect via mean differences (Theory)
The prima facie effect
Two kinds of biases
The role of randomization
Heterogeneity of variances between treatment groups
Estimating and testing average causal effects via structural equation modeling (Applications using LISREL and Mplus)
One outcome variable, two or more treatment groups
Several outcome variables, two or more treatment groups
One latent outcome variable, two or more treatment groups
Ordinal outcome variables, two or more treatment groups
Conditional Causal Effects (Theory)
Conditional (average) causal effects
Conditional mean differences
Identifying conditional and average causal effect via conditional mean differences (Theory)
Sufficient conditions for unbiasedness of conditional mean differences
Understanding the sufficient conditions
Estimating
and testing conditional and average causal effects via structural
equation modeling (Applications using LISREL and Mplus)
Three kinds of covariates: manifest continuous, manifest nominal, latent continuous
Analysis of covariance, regression with interactions, nonorthogonal analysis of variance
One outcome variable, two or more treatment groups, one covariate
Several outcome variables, two or more treatment groups, one covariate
One latent outcome variable, two or more treatment groups, one covariate
Ordinal outcome variables, two or more treatment groups, one covariate
Analysis of Individual and Average Causal Effects (Theory and Applications)
A Single Group, two Pre-test and two Post-test Occasions
Two Groups, two Pre-test and two Post-test Occasions
Steyer, R., Gabler, S., von Davier, A., Nachtigall, C. & Buhl,
T. (2000a) Causal regression models I: individual and average causal
effects. Methods of Psychological Research-Online, 5, 2, 39-71. (http://www.dgps.de/fachgruppen/methoden/mpr-online/)
Steyer, R., Gabler, S., von Davier, A. & Nachtigall, C. (2000b)
Causal regression models II: unconfoundedness and causal unbiasedness.
Methods of Psychological Research-Online, 5, 3, 55-86. (http://www.dgps.de/fachgruppen/methoden/mpr-online/)
Steyer, R., Nachtigall, C., Wüthrich-Martone, O. & Kraus, K.
(2002). Causal regression models III: covariates, conditional and
unconditional average causal effects. Methods of Psychological Research-Online, 7, 1, 41-68. (http://www.dgps.de/fachgruppen/methoden/mpr-online/)
Steyer, R., Flory, F., Klein, A., Partchev, I., Yousfi, S., Müller,
M. & Kröhne, U. (2004). Testing Average Effects in Regression
Models with Interactions. Eingereicht.
Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equation Models. Methodology European Journal of Research Methods for the Behavioral and Social Sciences, Vol.1(1), 39-54.
Einführung in die Analyse kausaler Effekte mit Strukturgleichungsmodellen (LISREL, Mplus)
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