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Testing hypotheses about direction of causation using cross-sectional family data
A C Heath1, R C Kessler, M C Neale
1Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63130.
Behavior Genetics
|January 1, 1993
Summary
Cross-sectional family data can reveal causal relationships between traits, especially when inheritance patterns differ. This approach offers an alternative when traditional causality studies are not feasible.
Area of Science:
- Behavioral Genetics
- Quantitative Genetics
- Causal Inference
Background:
- Correlated traits often raise questions about direction of causation.
- Traditional methods for establishing causality (intervention, prospective studies) are not always feasible.
- Cross-sectional family data offer a potential alternative for causal inference.
Purpose of the Study:
- To review conditions under which cross-sectional family data can inform direction of causation.
- To explore the utility of family data in testing unidirectional and reciprocal causation hypotheses.
- To identify potential sources of inferential error and power considerations.
Main Methods:
- Review of theoretical conditions for causal inference from family data.
- Analysis of trait inheritance patterns (genetic vs. family background).
- Power analyses considering measurement error.
Main Results:
- Family data are informative about causation when traits have different modes of inheritance.
- Unidirectional and reciprocal causation hypotheses can be tested under specific conditions.
- Multiple indicator variables are often necessary for adequate statistical power.
Conclusions:
- Cross-sectional family data provide a valuable tool for causal inference when conventional methods are impractical.
- Careful consideration of inheritance patterns and potential errors is crucial for valid conclusions.
- The approach is particularly useful in behavioral and quantitative genetics research.