From mksc-skills
Guides the choice among structural econometrics, analytical modeling, and causal ML methods for marketing science manuscripts, making models estimable and identified.
How this skill is triggered — by the user, by Claude, or both
Slash command
/mksc-skills:mksc-methodsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- You must choose between a structural, analytical, or reduced-form/causal-ML approach
Marketing Science is methodologically plural around a modeling core: structural econometrics, analytical models, econometric/statistical analysis, ML tools, surveys, and experiments — all judged by whether they develop, test, or rigorously apply a formal model.
| Claim / goal | Approach that earns it |
|---|---|
| Quantify demand and simulate a policy | Structural demand (BLP/mixed logit), supply FOCs, counterfactual |
| Forward-looking behavior, adoption, churn | Dynamic discrete choice / dynamic games (Rust, BBL, CCP) |
| Strategic-interaction insight, comparative statics | Analytical (game-theoretic) model |
| Bidding, sponsored search, marketplaces | Auction/structural-IO model with equilibrium bidding |
| Heterogeneous treatment effects tied to a model | Causal ML (double/debiased ML, causal forests) disciplined by theory |
| Causal effect from field variation | Field experiment / quasi-experiment as identifying variation |
A field experiment or quasi-experiment is welcome when it identifies a model primitive or validates a mechanism, not as a stand-alone reduced-form result.
Specify the equilibrium concept, solve it, and prove the claims; relegate long proofs to an appendix but state the key steps. Plan to validate counterintuitive predictions and discuss robustness to the modeling assumptions that drive them.
Use this as a second-pass capability check. First lock the demand/supply mechanism, fit evidence, and counterfactual decision margin; then test whether the manuscript addresses quantitative marketing reviewers who read the model through the managerial counterfactual it makes possible.
claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.【Genre】structural / analytical / causal-ML / experiment
【Model→estimator】GMM / MLE-SMLE / SMM / hierarchical Bayes
【Identification】instruments/variation → parameters; exogeneity defense
【Normalizations/assumptions】substantive vs. convenience
【Computation】solver, fixed point, starting values, multiplicity
【Next step】mksc-data-analysis
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin mksc-skillsBuilds formal analytical (game-theoretic) or structural econometric models for Marketing Science manuscripts, turning a marketing phenomenon into a testable model with identification arguments.
Matches research design to causal claims for JMR manuscripts: experimental design, causal identification (IV/DiD/RDD/matching), and structural estimation. Adapts to JMR's rigor and replication expectations.
Guides selection and defense of analytical or empirical methods for Management Science manuscripts, matching the question to the appropriate Department standard.