Testing Event-Based Forms of Causality.

Area of Science:

• Causal inference
• Statistical modeling
• Empirical data analysis

Background:

• Causal relations are fundamental in science, often categorized as necessity, sufficiency, or both.
• Traditional methods using Boolean algebra or statement calculus analyze these relations in categorical contexts.
• Analyzing empirical data for causal patterns requires robust statistical approaches.

Purpose of the Study:

• To propose Configural Frequency Analysis (CFA) as a method for testing hypotheses about causal relations.
• To develop CFA models for analyzing causality in both two-variable and multi-variable empirical datasets.
• To provide a framework for empirical data analysts to investigate necessity, sufficiency, and combined causal types.

Main Methods:

• Configural Frequency Analysis (CFA) is employed to test hypotheses regarding causal relations.
• Two distinct CFA approaches are presented: examining individual configurations and comparing supportive vs. non-supportive patterns.
• Models are developed for analyzing causal relationships involving two or more variables.

Main Results:

• Configural Frequency Analysis (CFA) effectively tests hypotheses about different types of causal relations.
• The proposed CFA approaches allow for the examination of specific event patterns supporting causality.
• An empirical example demonstrated the application of CFA in predicting the sustainability of dietary habit changes.

Conclusions:

• Configural Frequency Analysis (CFA) offers a powerful tool for empirical analysis of causal relations.
• The method supports the identification and testing of necessity and sufficiency in categorical data.
• CFA provides a valuable framework for understanding complex causal patterns in real-world data.