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This lecture is a second course in probability and causality. In the first course the emphasis was on data analysis with EffectLiteR, a computer program for the analysis of conditional and average effects. The probabilistic theory of causal effects was treated but not in much details. In this second course, the focus is on the probabilistic theory of causal effects. It is based on the book "Probability and causality" that is currently in preparation.

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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., Nagel, W., Partchev, I., & Mayer, A. (in preparation). Probability and Regression.

Date	Topic	Video	Material
2016-04-04	Why causality? Simpson's paradox Nonorthogonal ANOVA A randomized experiment with direct and indirect effects How to compute regression coefficients from the variance-covariance matrix	Video (Stream)	Probability and Causality (till chapter 1) Probability and Conditional Expectation Blackboard sketch 01 Blackboard sketch 02
2016-04-11	Single-unit trials of some typical designs in psychological research Covariates and intermediate variables in these single-unit trials Treatment variables in these single-unit trials Outcome variables in these single-unit trials Causal effects and dependencies in these single-unit trials	Video (Stream)	Probability and Causality (till chapter 2) Blackboard sketch
2016-04-18	The structural components of a causality space Filtration Priority and simultaneity relations among sets (events), set systems, and random variables Covariate	Video (Stream)	Probability and Causality (till chapter 3) Blackboard sketches
2016-04-25	Confounder Sigma-Algebra of X with respect to a filtration Covariate of X with respect to a filtration Intermediate variable of X and Y with respect to a filtration (X=x)-conditional probability measure PX=x Conditional expectation of Y given the confounder sigma-algebra, with respect to PX=x P-uniqueness and PX=x-uniqueness of this conditional expectation Total-effect true outcome variable	Video (Stream)	Blackboard sketches
2016-05-02	Preliminary considerations on the definition of the potential confounder sigma-algebra of X with respect to time tM and on the definition of the direct-effect true-outcome variables with respect to time tM	Video (Stream)	Blackboard sketches
2016-05-09	The potential confounder sigma-algebra of X with respect to time tM Average total effect (V=v)-conditional total effect V-conditional total-effect function Average direct effect with respect to time tM (V=v)-conditional direct effect with respect to time tM V-conditional direct-effect function with respect to time tM Average indirect effect with respect to time tM (V=v)-conditional indirect effect with respect to time tM V-conditional indirect-effect function with respect to time tM	Video (Stream)	Probability and Causality (till chapter 5) Blackboard sketches
2016-05-23	Unbiasedness With Respect to Total Effects Bias With Respect to Total Effects Baseline Bias and Effect Bias Unbiasedness With Respect to Direct Effects	Video (Stream)	Probability and Causality (till chapter 6) Blackboard sketches
2016-05-30	Two causality conditions: Independent cause and completeness The implications of these two conditions for unbiasedness	Video (Stream)	Blackboard sketches
2016-06-06	Two causality conditions: Independent cause and completeness The implications of these two conditions for unbiasedness	Video (Stream)	Probability and Causality (chapter 6) Blackboard sketches
2016-06-13	Summary of the first two causality conditions Analysis of change variables	Video (Stream)	Probability and Causality (chapter 7) Blackboard sketch 01 Blackboard sketch 02
2016-06-20	True propensity True propensity as a covariate Adjustment using the logit transformation of the true propensity illustrated by the Klauer data. Some questions concerning mediator models.	Video (Stream)	Blackboard sketches
2016-06-27	An example of a mediator model (student presentation).	Video (Stream)	Blackboard sketch 01 Blackboard sketch 02
2016-07-04		Video (Stream)	Actual version of the C-book Blackboard sketches