Kurse: Kursinformationen

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)

1. A Single Group, two Pre-test and two Post-test Occasions
2. Two Groups, two Pre-test and two Post-test Occasions
3. Extensions

Latent state-trait theory of Individual and Average Causal Effects (Theory)

Latent state-trait analysis of Individual and Average Causal Effects (Applications)

Recorded videos

Session	Topic	Abstract (PDF)	Presenter	Video-presentation with slides (Realplayer)	Video (RealPlayer)	Slides
01	Why conditional and average effects?	Abstract	Rolf Steyer	Video with slides 01	Video 01	A 1 Why Conditional and Average Effects.pdf
02	How to use EffectLite for the analysis of unconditional, conditional and average effects	Abstract	Rolf Steyer	Video with slides 02	Video 02	A 1 Why Conditional and Average Effects.pdf
03	Input and Output files (LISREL and Mplus) produced by EffectLite	Abstract	Rolf Steyer & Ivailo Partchev	Video with slides 03	Video 03	A 2 Individual and Average Causal Effects.pdf
04	Using EffectLite with latent covariates, latent outcome and several manifest covariates	Abstract	Rolf Steyer & Ivailo Partchev	Video with slides 04	Video 04
05	Identifying the average causal effect via mean differences	Abstract	Rolf Steyer	Video with slides 05	Video 05	A 2 Individual and Average Causal Effects.pdf A 3 Identifying the Average Causal Effect.pdf
06	Conditional Causal Effects: Theoretical Foundations and Applications	Abstract	Rolf Steyer	Video with slides 06	Video 06
07	Analysis of Conditional and Average Causal Effects With Latent Covariates Using LISREL	Abstract	Rolf Steyer	Video with slides 07	Video 07
08	Analysis of Conditional and Average Causal Effects With Latent Covariates Measured by Ordinal Variables	Abstract	Rolf Steyer & Ivailo Partchev	Video with slides 08	Video 08
09	Analysis of Conditional and Average Causal Effects With Manifest Covariates: Real Data Examples	Abstract	Rolf Steyer	Video with slides 09	Video 09
10	Analysis of Individual and Average Causal Effects	Abstract	Rolf Steyer	Video with slides 10	Video 10	A 5 Analysis of Individual and Average Effects.pdf
11	Analysis of Individual Causal Effects	Abstract	Steffi Pohl & Safir Yousfi	Video with slides 11	Video 11
12	Analysis of Individual Causal Effects	Abstract	Steffi Pohl & Safir Yousfi	Video with slides 12	Video 12
13	Analysis of Individual and Average Causal Effects	Abstract	Rolf Steyer	Video with slides 13	Video 13	Latent State-Trait Models.pdf A 7 Theory of causal regressions.pdf
14	Analysis of Individual and Average Causal Effects	Abstract	Rolf Steyer & Andreas Wolf	Video with slides 14	Video 14	A 8 Latent growth curves.pdf
15	Dynamic individual causal effect models	Abstract	Safir Yousfi		Video 15
16	Dynamic individual causal effect models	Abstract	Safir Yousfi	Video with slides 16	Video 16

Material

Readings and more
A 1 Why Conditional and Average Effects.pdf
A 2 Individual and Average Causal Effects.pdf
A 3 Identifying the Average Causal Effect.pdf
A 4 Conditional Causal Effects.pdf
A 5 Analysis of Individual and Average Effects.pdf
Analyzing Individual and Average Causal Effects Printed version.pdf
LISREL Command Syntax.pdf
Excelsheet Ben March 4.xls
Testing average effects Methodology Version 7-1.pdf
Latent State-Trait Models.pdf
A 7 Theory of causal regressions.pdf
A 8 Latent growth curves.pdf

Data sets
Brief description of simulated examples.pdf
Detailed description of simulated examples.pdf
dset1.sav
dset2.sav
dset3.sav
Scheikurz.SPS
scheikurzneu.csv
scheikurzneu.sav
scheikurzneu.xpt
simulex1n100.csv
simulex1n100.R
simulex1n1000.csv
simulex1n1000.R
simulex1n5000.csv
simulex1n5000.R
simulex2n100.csv
simulex2n100.R
simulex2n1000.csv
simulex2n5000.csv
simulex3n1000.csv
simulex3n1000.R

EffectLite
http://www.statlite.com/

References

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. Paper.

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.