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Setting Limits on Supersymmetry Using Simplified Models

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Published on: November 15, 2013

The probability of simple versus complex causal models in causal analyses.

David Trafimow¹

¹Department of Psychology, New Mexico State University, Las Cruces, Mexico. dtrafimo@nmsu.edu.

Behavior Research Methods

|April 10, 2016

Summary

Complex causal models are often seen as more credible in social sciences, despite probability axioms suggesting simpler models may be more scientifically sound. This challenges the perception of complexity equaling scientific rigor.

Keywords:

Causal analysis Causal structure Complex causal model Conjunction fallacy Simple causal model

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Area of Science:

Social Sciences
Statistics
Causal Inference

Background:

Social sciences frequently employ complex causal models with large correlation matrices.
Simple causal models with single correlation coefficients are less common.

Purpose of the Study:

To investigate the relationship between model complexity and scientific credibility in causal analysis.
To examine the implications of probability axioms on the perceived validity of complex versus simple causal models.

Main Methods:

Analysis of common practices in social science research regarding causal modeling.
Application of probability axioms to evaluate causal inference approaches.

Main Results:

Complex causal models and analyses are prevalent in social sciences, often associated with higher perceived credibility.
Probability theory suggests that simpler models might offer a more robust or credible foundation.

Conclusions:

There is a divergence between the common practice in social sciences, favoring complex models, and theoretical statistical principles.
The perceived scientific respectability of complex causal models may not align with statistical axioms.