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Causal Effects EAM Fachgruppe Methoden und Evaluation (DGPs)

Kurse: Kursinformationen

en  Introduction to Causal Modeling with Structural Equation Models (LISREL, Mplus)

Kursleitung: Prof. Dr. Rolf Steyer, Dr. Ivailo Partchev, Dr. Safir Yousfi & Dipl.-Psych. MSc Steffi Pohl

Wintersemester 2004/2005, Workshop, Kurslänge: 22.50 Stunden, Sprache: Englisch, Thema: Analysis of causal effects

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?
Rolf Steyer
02
How to use EffectLite for the analysis of unconditional, conditional and average effects
Rolf Steyer
03
Input and Output files (LISREL and Mplus) produced by EffectLite
Rolf Steyer & Ivailo Partchev
04
Using EffectLite with latent covariates, latent outcome and several manifest covariates
Rolf Steyer & Ivailo Partchev
 
05
Identifying the average causal effect via mean differences
Rolf Steyer
06
Conditional Causal Effects: Theoretical Foundations and Applications
Rolf Steyer
 
07
Analysis of Conditional and Average Causal Effects With Latent Covariates Using LISREL
Rolf Steyer
 
08
Analysis of Conditional and Average Causal Effects With Latent Covariates Measured by Ordinal Variables
Rolf Steyer & Ivailo Partchev
 
09
Analysis of Conditional and Average Causal Effects With Manifest Covariates: Real Data Examples
Rolf Steyer
 
10
Analysis of Individual and Average Causal Effects
Rolf Steyer
11
Analysis of Individual Causal Effects
Steffi Pohl & Safir Yousfi
 
12
Analysis of Individual Causal Effects
Steffi Pohl & Safir Yousfi
 
13
Analysis of Individual and Average Causal Effects
Rolf Steyer
14
Analysis of Individual and Average Causal Effects
Rolf Steyer & Andreas Wolf
15
Dynamic individual causal effect models
Safir Yousfi
 
16
Dynamic individual causal effect models
Safir Yousfi
 


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.