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Here are the links/downloads to the books and some other articles.

Current versions

	Steyer. R. & Nagel, W. (2017). Probability and conditional expectation: Fundamentals for the empirical sciences. Chichester: Wiley.
	Errata to Probability and Conditional Expectation: Fundamentals for the Empirical Sciences. Chichester: Wiley.
	A reader's letter about the book: Steyer. R. & Nagel, W. (2017). Probability and Conditional Expectation: Fundamentals for the Empirical Sciences. Chichester: Wiley.
	Videos and courses to the book: Steyer. R. & Nagel, W. (2017). Probability and Conditional Expectation: Fundamentals for the Empirical Sciences. Chichester: Wiley.

	Steyer, R. (2021; in preparation). Introduction to Probability and Conditional Expectation. Book in preparation.
	Steyer, R. (2019; in preparation). Probability and Causality. Conditional and Average Total Effects. Book in preparation.

Articles

	Axel Mayer & Felix Thoemmes (2019) Analysis of Variance Models with Stochastic Group Weights, Multivariate Behavioral Research, 54:4, 542-554, DOI: 10.1080/00273171.2018.1548960
	Mayer, A., Thoemmes, F., Rose, N., Steyer, R., & West, S. G. (2014). Theory and analysis of total, direct and indirect causal effects. Multivariate Behavioral Research, 49, 425-442. (Online appendix, data generation, dataset, Mplus (Muthén & Muthén, 1998-2012) & lavaan (Rosseel, 2012) input files to this article)
	Steyer, R. (2005). Analyzing individual and average causal effects via structural equation models. Methodology, 1(1), 39-54.
	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.
	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.
	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.

EffectLiteR

	Sengewald, M.-A. & Mayer, A. (2022). Causal effect analysis in nonrandomized data with latent variables and categorical indicators: The implementation and benefits of EffectLiteR. Psychological Methods.
	Wester, R. A., Rubel, J., & Mayer, A. (2022). Covariate selection for estimating individual treatment effects in psychotherapy research: A simulation study and empirical example. Clinical Psychological Science.
	Graefe, L., Hahn, S., & Mayer, A. (2022). On the relationship between ANOVA main effects and average treatment effects. Multivariate Behavioral Research, 1–17.
	Kiefer, C., & Mayer, A. (2021). Treatment effects on count outcomes with non-normal covariates. British Journal ofMathematical and Statistical Psychology, 74, 513–540.
	Keck, C., Mayer, A., & Rosseel, Y. (2021). Integrating informative hypotheses into the EffectLiteR framework. Methodology, 17, 307–325
	Langenberg, B., Helm, J. L., & Mayer, A. (2022). Repeated measures ANOVA with latent variables to analyze interindividual differences in contrasts. Multivariate Behavioral Research, 57, 2–19.
	Mayer, A., Zimmermann, J., Hoyer, J., Salzer, S.,Wiltink, J., Leibing, E., & Leichsenring, F. (2020). Interindividual differences in treatment effects based on structural equation models with latent variables: An EffectLiteR tutorial. Structural Equation Modeling.
	Mayer, A. (2019). Causal effects based on latent variable models. Methodology, 15, 15–28.
	Mayer, A. & Thoemmes, F. (2019). Analysis of variance methods with stochastic group weights. Multivariate Behavioral Research, 54, 543–554.
	Kiefer, C. & Mayer, A. (2019). Average effects based on regressions with a logarithmic link function: A new approach with stochastic covariates. Psychometrika, 84, 422–446.
	Flunger, B., Mayer, A., & Umbach, N. (2019). Beneficial for some or for everyone? Student characteristics as moderators of an autonomy-supportive intervention in the classroom. Journal of Educational Psychology, 111, 210–234.
	Mayer, A., Umbach, N., Flunger, B., & Kelava, A. (2017). Effect analysis using non-linear structural equation mixture modeling. Structural Equation Modeling, 24, 556–570.
	Holtforth, M.G., Wilm, K., Beyermann, S., Rhode, A., Trost, S., & Steyer, R. Differential change in integrative psychotherapy: a re-analysis of a change-factor based RCT in a naturalistic setting. Psychotherapy Research, 21, 631-643.
	Karing, C. & Beelmann, A. (2016). Implementation und Evaluation eines multimodalen Stressbewältigungstrainings bei Lehramtsstudierenden. Zeitschrift für Gesundheitspsychologie, 24(2), 89-101
	Mayer, A., Dietzfelbinger, L., Rosseel, Y., & Steyer, R. (2016). The EffectLiteR approach for analyzing average and conditional effects. Multivariate Behavioral Research, 51, 374-391.
	Müller, B., Mayer, A., Richter, T., Križan, A., Hecht, T., & Ennemoser, M. (2015). Differential effects of reading trainings on reading processes: A comparison in Grade 2. Zeitschrift für Erziehungswissenschaft, 18, 489-512.
	Mayer, A., Nagengast, B., Fletcher, J., & Steyer, R. (2014). Analyzing average and conditional effects with multigroup multilevel structural equation models. Frontiers in Quantitative Psychology and Measurement, 5.
	Mayer, A., Thoemmes, F., Rose, N., Steyer, R., & West, S. G. (2014). Theory and analysis of total, direct and indirect causal effects. Multivariate Behavioral Research, 49, 425–442.
	Kirchmann, H., & Steyer, R. (2012). Evaluation von Behandlungseffekten in quasi-experimentellen Studien. PPmP-Psychotherapie - Psychosomatik - Medizinische Psychologie, 62, 149-151.
	Kirchmann, H., Steyer, R., Mayer, A., Joraschky, P., Schreiber-Willnow, K., & Strauss, B. (2011). Effects of adult inpatient group psychotherapy on attachment characteristics: An observational study comparing routine care to an untreated comparison group. Psychotherapy Research, 22(1), 95-114.