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Topic: Analysis of conditional and average total causal treatment effects

1. Why we need a theory of causal effects
2. Concepts of probability theory
3. Basic ideas of the theory of causal effects
4. The core of the theory of causal effects
5. Causality conditions (sufficient conditions for unbiasedness), randomization, and covariate selection
6. First example: nonorthogonal analysis of variance
7. Intercept function, effect functions, and average effects in the example of nonorthogonal analysis of variance
8. Estimating intercept function, effect functions, and average effects via SPSS and its limitations
9. Analyzing the data of nonorthogonal analysis of variance with SPSS ANOVA (Typ I, II, III sums of squares) and why these analyses yields wrong results
10. The hypothesis of no treatment effects via SPSS using the R²-difference test
11. Analysis of the data of nonorthogonal analysis of variance with EffectLiteR
12. Second example: The Kirchmann study on the treatment effects on depression and its analysis with EffectLiteR
13. Third example: The Klauer study on the training of inductive reasoning
14. EffectLiteR analysis of the Klauer data with a continuous covariate
15. Main hypotheses in EffectLiteR and the various conditional and average treatment effects
16. EffectLiteR analysis of the Klauer data with a continuous covariate and a qualitative covariate

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Literature

Causal effects

• Campbell, D. T. & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research on Teaching. In N. L. Gage (Ed.), Handbook of research on teaching. Chicago: Rand McNally.
• West, S. G., Biesanz, J. C. & Pitts, S. C. (2000), Causal inference and generalization in field settings. Experimental and quasi-experimental designs. In H. T. Reis and C. M. Judd (eds.), Handbook of research methods in social and personality psychology. Cambridge University Press.
• Steyer, R. (2003). Wahrscheinlichkeit und Regression. Berlin: Springer. (Kapitel 15 - 17)
• Steyer, R. (2004). Was wollen und was können wir durch empirische Kausalforschung erfahren? In E. Erdfelder & J. Funke (Hrsg.), Allgemeine Psychologie und deduktivistische Methodologie (pp.127-147). Göttingen: Vandenhoek und Ruprecht.
• Steyer, R. (2005). Analyzing Individual and Average Causal Effects via Structural Equa­tion Models. Methodology-European Journal of Research Methods in the Behavio­ral and Social Sciences, 1, 39-54.
• Steyer, R. & Partchev, I. (2006). Manual for EffectLite: A Program for the Uni- and Multivariate Analysis of Unconditional, Conditional and Average Mean Differences Between Groups.
• Pohl, S., Steyer, R. & Kraus, K. (2008). Modelling method effects as individual causal effects. Journal of the Royal Statistical Society. Series A, 171, 41--63.
• Steyer, R., Partchev, I., Kröhne, U., Nagengast, B., & Fiege, C. (in preparation). Probability and Causality.
• Steyer. R. and Nagel, W. (2017). Probability and conditional expectation: Fundamentals for the empirical sciences. Chichester: Wiley.

Date	Topic	Video	Material
2015-10-19	Why we need a theory of causal effects Example Joe and Ann with self-selection Random experiment Set of possible outcomes of a random experiment Event Probability of an event Conditional probability of an event Random variable Expectation of a discrete random variable Conditional expectation of a discrete random variable	Video	Slides (updated 2015-10-26) Blackboard sketches
2015-10-26	Basic Ideas of the theory of total causal effects Kirchmann example Individual total causal Effect Average total causal Effect	Video	Probability and Conditional Expectation Materials (updated 2015-10-27) Blackboard sketches
2015-11-02	The core of the theory of total causal effects Covariate The random experiment (the empirical phenomenon) considered Examples in which one of the causality conditions for E(Y|X,Z) is satisfied Unbiasedness of E(Y|X,Z) and EX=x(Y|Z) Implications unbiasedness for the identification of conditional and average causal total effects.	Video	Slides (updated 2015-11-10)
2015-11-09	Four causality conditions for E(Y|X,Z) The experimental design technique of conditional randomization Covariate selection based on the causality conditions The example of nonorthogonal analysis of variance: Conditional and average total treatment effects	Video	Dataset Blackboard sketch 01 Blackboard sketch 02
2015-11-16	Intercept function and conditional-effect functions in the nonorthogonal ANOVA Example Parameterization of the intercept function and conditional-effect functions in this example Analysis of conditional effects in the nonorthogonal ANOVA Example with the Linear Regression program of SPSS Point estimation of the conditional effects based on this data analysis Limitations of the analysis conditional treatment effects via Linear Regression: No standard errors of conditional effects, no average treatment effects	Video	SPSS-Output Blackboard sketches
2015-11-23	The distinction between fixed and stochastic regressors. Data analysis with (nonorthogonal) ANOVA (SPSS): Type I, II, III, and IV of decomposing the sum of squares. All of them do not test the hypothesis that the average treatment effect is zero. Hypotheses that are tested as the so-called main effects with Typ I, II, and III. Summarizing the basic concepts and equations in the analysis of conditional and average effects.	Video	Slides SPSS-Output Blackboard sketches
2015-11-30	R2-Difference test for the hypothesis "There are no treatment-effects" Point estimate for the average treatment effects in SPSS Analysis of the nonortho-data with EffectLiteR Interpretation of the first results of this EffectLiteR-Analysis: Average effects and conditional effects A first analysis of the Kirchmann-data set with EffectLiteR	Video	Materials Blackboard sketches
2015-12-07	Conditional effects in the example of nonorthogonal Anova (continued) Conditional effects given a treatment condition Analysis of the Klauer data with a continuous covariate (pretest CFT) and an outcome variable (posttest CFT) Assuming linearity of the g-functions Interpretation of the main hypotheses in terms of the g-funktions and the gamma-coefficients Conditional effect given a pretest score	Video	Slides Dataset Blackboard sketches
2015-12-14	Checking linearity of the g-functions with EffectLiteR Testing linearity of the g-functions with SPSS (R2-difference test) (X=x)-conditional treatment effects When should we consider average effects and when (X=x)-conditional treatment effects? Basic idea and assumptions in the EffectLiteR analysis with qualitative and quantitative covariates	Video	Materials Blackboard sketches
2016-01-04	EffectLiteR analysis of the Klauer data with a qualitative and a quantitative covariate Model equation and linearity assumption for the regression of the outcome variable on the quantitative covariate in each cell Meaning of the four main hypotheses in terms of (a) expected values or effects, (b) the g-functions, and (c) the coefficients of the g-functions Adjusted expected values Conditional effects given values of the qualitative covariate Conditional effects given values of the qualitative covariate and the treatment variable Conditional effects given values of the qualitative covariate and the quantitative covariate Conditional expected values of the outcome variable under treatment and under control given values of the qualitative covariate and the quantitative covariate	Video	Dataset Blackboard sketch 01 Blackboard sketch 02
2016-01-11	EffectLiteR analysis of the Klauer data: one qualitative and one quantitative covariate (continued) Histograms of the dependent variable in the cells Regression of the dependent variable on the continuous covariate in each cell of the design Scattergram of the conditional effects and the continuous covariate Re-aggregation of the conditional effects in the context of the theory of causal effects EffectLiteR analysis of the Klauer data: one qualitative and two quantitative covariates Basic concepts and models of classical test theory	Video	Slides 1 Slides 2 -Steyer, R., Mayer, A., Geiser, C., & Cole, D. (2015). A Theory of States and Traits—Revised. Annual Review of Clinical Psychology, 11, 71-98. Blackboard sketches
2016-01-18	EffectLiteR analysis of the Klauer data with a latent covariate and a latent outcome variable. Models of essentially tau-equivalent and tau-congeneric variables Testing the model with a goodness-of-fit test and the RMSEA Conditional effects given estimated values of the latent covariates EffectLiteR analysis of the Klauer data with two latent covariates and a latent outcome variable EffectLiteR analysis of the Klauer data with a latent covariate, a method factor, and a latent outcome variable	Video	Blackboard sketches
2016-01-25	Method factor as an additional latent covariate (continued) Method factor with a reference method and method factor with a common factor An EffectLiteR analysis with a qualitative and a latent covariate Under which circumstances should we use a model with a latent instead of a manifest covariate?	Video	Blackboard sketch 01 Blackboard sketch 02
2016-02-01	Combining the theory of causal Effects with EffectLiteR Analyses True-Outcome-Variables for total effects Atomic causal total effects Average causal total effects Conditional causal total effects Unbiasedness of the conditional expectations EX=x(Y|Z) Implications of Unbiasedness Two sufficient conditions for unbiasedness Testing the sufficient conditions for unbiasedness	Video	Slides SPSS-Output Blackboard sketch 01 Blackboard sketch 02
2016-02-08		Video	Blackboard sketches