Causal Effects
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Here you can download some tools related to the books.


EffectLite is a PC-program for the uni- and multivariate analysis of mean differences between groups on outcome (or response) variables in designs with and without covariates. If there is no covariate, then the unconditional mean differences are analyzed. If there is at least one covariate, the conditional and the average (or 'adjusted') mean differences are analyzed.

EffectLite has been developed for an easy and better analysis of pretest-treatment-posttest designs with two or more treatment groups. Designs with several quantitative pretests or qualitative blocking factors, several dependent (outcome) variables and a treatment variable with several levels can be analyzed easily.

EffectLite has been developed by Rolf Steyer (statistics) and Ivailo Partchev (programming) at the University of Jena, Germany. It acts as a pre- and postprocessor for LISREL (Version 8) or MPlus (Version 3), so its full functionality is achieved only when at least one of these two programs is installed.

Icon: WinZip EffectLite for LISREL [v3.1.2 - 2012-03-01] Icon: WinZip EffectLite for Mplus [v3.1.2 - 2012-03-01]
Icon: Adobe PDF Manual for EffectLite for LISREL [v3.1.1 - 2008-03-27] Icon: Adobe PDF Manual for EffectLite for Mplus [v3.1.1 - 2008-03-27]


A program for the analysis of conditional and average effects of a discrete treatment or intervention variable. This freeware program uses R and lavaan, estimating conditional and average effects given qualitative and/or quantitative covariates.

Icon: External-website EffectLiteR package at GitHub (If you have R installed already)
Icon: WinZip EffectLiteR-Standalone Installer with R (If you not installed R already)
Icon: Website Publications to EffectLiteR

Causal Effects Xplorer - A Didactic tool for teaching the theory of individual and average causal effects

This is a slightly modified version of a didactic tool for teaching the theory of individual and average causal effects (Nagengast, 2005). The user can set up a three-group design by specifying expected outcome variables under treatment and under control for each of up to eight units. Individual sampling and assignment probabilities can be fixed independently for every unit. The program then calculates - for the whole group and for each subgroup if a covariate is specified - the causally unbiased expected values under treatment and under control as well as individual and average causal effects. Furthermore the expected values under treatment and under control, the prima facie effects, baseline and differential effects biases are computed automatically. Additionally, several criteria for causal unbiasedness of the unconditional and conditional expected values are tested.

Nagengast, B. (2005, July). A didactic tool for teaching the theory of individual and average causal effects. Talk given at the 14th International Meeting of Psychometric Society, IMPS 2005, Tilburg, Netherlands.

Icon: WinZip Causal Effects Xplorer [v0.6.4 - 2019-02-27]
Icon: Adobe PDF Manual for Causal Effects Xplorer [v0.6.3 - 2018-02-23]

Aggregation[0,1]Xplorer for binary response variable

The "Aggregation[0,1]Xplorer for binary response variable" is an interactive tool to explore the effect of a binary treatment variable on a binary response variable, while aggregating over the values of a covariate.

The accompanying bachelor thesis provides more information about the theoretical background, usage instructions and further references. !! only available in German language

Icon: External-website Aggregation[0,1]Xplorer at GitLab
Icon: Adobe PDF Masters thesis to the Aggregation[0,1]Xplorer by Matthias Gehlert

Process Explorer

An application for studying issues of causal effects in discrete-time and continuous-time stochastic processes.

Icon: WinZip Process Explorer Installer [2018-07-19]
Icon: Adobe PDF Masters thesis to the Process Explorer by Julia Gantner