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1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; Harvard-MIT Division of Health Sciences and Technology, Boston, MA.
Annals of Epidemiology
|September 20, 2016
Summary
Scientists can determine causal effects using the potential outcomes approach. This article explains the consistency condition, crucial for interpreting results in real-world decision-making.
Area of Science:
- Causal inference
- Quantitative theory
- Decision science
Background:
- Interpreting quantitative results as causal effects is a critical challenge.
- The potential outcomes approach provides a framework for causal inference.
Purpose of the Study:
- To review the consistency condition within the potential outcomes framework.
- To discuss the implications of consistency for practical decision-making.
Main Methods:
- Review of the potential outcomes approach in causal inference.
- Explanation of the consistency condition and its assumptions.
Main Results:
- Consistency is a necessary condition for identifying causal effects.
- Understanding consistency clarifies when observed data can inform causal questions.
Conclusions:
- The consistency condition is vital for valid causal effect estimation.
- Applying the concept of consistency enhances the reliability of real-world decisions based on quantitative data.
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