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Campbell's and Rubin's perspectives on causal inference.
Stephen G West1, Felix Thoemmes
1Psychology Department, Arizona State University, Tempe, AZ 85287-1104, USA. sgwest@asu.edu
Psychological Methods
|March 17, 2010
Summary
Donald Campbell's and Donald Rubin's causal inference approaches offer complementary strengths for research design. Integrating both enhances the validity and generalizability of causal effect estimation in various study types.
Area of Science:
- Social Sciences
- Statistics
- Public Health
Background:
- Two prominent causal inference frameworks exist: Donald Campbell's approach, prevalent in psychology and education, and Donald Rubin's model, widely adopted in statistics, economics, medicine, and public health.
- Campbell's method emphasizes identifying and mitigating threats to research validity.
- Rubin's model focuses on precisely defining potential outcomes and mathematical assumptions for causal effect estimation.
Purpose of the Study:
- To compare the perspectives of Campbell's and Rubin's causal inference approaches.
- To analyze how each framework addresses randomized experiments, broken randomized experiments (with nonadherence or attrition), and observational studies.
- To highlight key differences in their emphases, including constructs vs. operations, validity threats vs. assumptions, and measurement roles.
Main Methods:
- Comparative analysis of Donald Campbell's and Donald Rubin's causal inference methodologies.
- Examination of their application to different experimental and observational study designs.
- Identification of divergent emphases across critical research design dimensions.
Main Results:
- Campbell's approach prioritizes validity threats and design features, while Rubin's focuses on potential outcomes and mathematical assumptions.
- Differences emerge in handling constructs, validity threats, assumption violations, effect direction/magnitude, measurement, and causal generalization.
- Both approaches offer distinct yet valuable perspectives on randomized and observational studies, including those with treatment nonadherence or attrition.
Conclusions:
- Campbell's and Rubin's causal inference frameworks provide complementary insights for research design.
- Researchers can benefit significantly by integrating the strengths of both approaches.
- A combined perspective enhances the robustness and applicability of causal effect estimation across diverse research contexts.
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