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Causal structure and hierarchies of models
1Department of Economics, Duke University, Durham, NC 27278, USA. kd.hoover@duke.edu
Summary
Economics often seeks complete causal explanations but sometimes uses simpler models. This study explores Herbert Simon
Area of Science:
- Economics
- Causation
- Econometrics
Background:
- Economics traditionally prefers comprehensive explanations, favoring general over partial equilibrium and microfoundations over aggregate models.
- Probabilistic causation theories often prioritize detailed causal accounts, similar to resolutions of Simpson's paradox and causal refinement strategies.
- Countervailing practices exist in economics, such as representative-agent models and smaller-scale structural vector-autoregression (SVAR) and dynamic stochastic general-equilibrium (DSGE) models.
Purpose of the Study:
- To address the tension between the preference for complete causal explanations and the practical use of simplified models in economics.
- To explore a structural account of causation, inspired by Herbert Simon, for understanding complex economic systems.
- To investigate the constraints on modeling complex systems as simpler ones and the integration of piecemeal evidence.
Main Methods:
- Utilizes a structural account of causation, drawing on Herbert Simon's work.
- Analyzes the relationship between complete systems and incomplete systems in economic modeling.
- Examines the role of exogenous and endogenous variables in partitioning economic systems.
Main Results:
- Identifies constraints imposed by a structural causation account on simplifying complex or lower-order economic systems.
- Assesses the extent to which piecemeal evidence can be incorporated into structural causal accounts.
- Highlights the practical preference for smaller-scale econometric models despite theoretical inclinations towards completeness.
Conclusions:
- A structural account of causation provides a framework for reconciling the desire for detailed explanations with the necessity of using simplified models in economics.
- Understanding these constraints is crucial for developing robust economic models that effectively incorporate available evidence.
- The study contributes to a deeper understanding of causal inference in economics, particularly when dealing with complex systems and limited data.
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