Qualitative Approximations to Causality: Non-Randomizable Factors in Clinical Psychology.

Area of Science:

• Epidemiology
• Biostatistics
• Clinical Psychology

Background:

• Causal inference is crucial for non-randomized studies, particularly in etiological research.
• Methodological advancements have expanded the tools for causal analysis in recent decades.

Purpose of the Study:

• To demonstrate how qualitative approaches can inform quantitative causal analyses.
• To explore the utility of qualitative methods in assessing the necessity and feasibility of quantitative studies.
• To show how qualitative insights can approximate causal answers in specific contexts.

Main Methods:

• Integration of qualitative approaches within quantitative frameworks.
• Application of counterfactuals to define causal effects of changeable factors.
• Utilizing directed acyclic graphs (DAGs) for broader causal effect analysis.
• Qualitative assessment of bias direction for tentative causal conclusions.

Main Results:

• Qualitative methods can guide the necessity and feasibility of quantitative causal analyses.
• Counterfactuals define causal effects for changeable factors but not immutable ones (e.g., gender).
• Directed acyclic graphs (DAGs) offer a broader perspective on causal effects, guiding quantitative estimation.

Conclusions:

• No single method is universally sufficient or necessary for causal analysis.
• Causal analysis requires qualitative grounding and context-specific balancing of false positive/negative risks.