Scientist's guide to developing explanatory statistical models using causal analysis principles

Area of Science:

• Ecology
• Statistics
• Scientific Methodology

Background:

• Model selection and multimodel inference present challenges in clearly communicating explanatory content.
• Statisticians advise developing well-thought-out candidate models but lack precise instructions for scientists.
• Causal analysis offers a framework for examining the explanatory content of scientific models.

Purpose of the Study:

• To summarize and illustrate principles from causal analysis for developing explanatory hypotheses.
• To bridge the communication gap between statisticians and scientists regarding model development.
• To provide practical guidance for scientists on creating well-formed hypotheses for evaluation.

Main Methods:

• Review and synthesis of causal analysis principles and graphical tools.
• Illustration of how these principles guide hypothesis development.
• Connecting causal analysis to existing methods like structural equation modeling.

Main Results:

• Causal analysis provides a coherent body of knowledge with graphical tools and axiomatic principles.
• These principles support scientists in creating "well-formed hypotheses."
• The presented principles can guide hypothesis development for evaluation against data.

Conclusions:

• Causal analysis offers practical guidance for scientists to develop explanatory hypotheses.
• This approach complements statistical evaluation methods like structural equation modeling.
• The principles can enhance the clarity and coherence of scientific models and communication.