domains / families / research / mksc-methods
mksc-methods
separate
Use when the empirical/analytical approach is the bottleneck for a Marketing Science manuscript — choosing among structural econometrics, analytical modeling, and model-disciplined causal/ML methods, and making the model estimable and identified. Designs the approach; it does not execute the estimation and counterfactuals (mksc-data-analysis).
When to use
- Choosing between structural, analytical, or reduced-form/causal-ML approaches
- Making a written model estimable with parameters, moments, and normalization
- Defining specific instruments and identifying variation
- Designing structural demand, dynamic discrete choice, or analytical game-theoretic models
- Specifying estimators like GMM, MLE, SMM, or hierarchical Bayes
- Planning computation including solvers, fixed points, and multiple equilibria
- Validating counterintuitive predictions in analytical papers
- Auditing empirical/causal lane designs using StatsPAI or Stata MCP
When not to use
- Executing the estimation and counterfactuals
- Structural demand and analytical modeling execution within the causal-inference toolchain
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