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smj-data-analysis
GitHub针对SMJ论文，执行并压力测试因果识别设计以解决内生性。涵盖DID、IV及匹配方法，验证平行趋势、排除约束等，确保研究满足顶级期刊对因果推断的严苛要求。
Trigger Scenarios
存在未处理内生性或反向因果的性能回归
已选择DID、IV或匹配模型但未进行压力测试
需规划机制检验与稳健性检验策略
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smj-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "smj-data-analysis",
"description": "Use when estimating models and defeating endogeneity for a Strategic Management Journal (SMJ) manuscript — the single highest bar at SMJ. Executes and stress-tests the identification design from smj-methods; it does not design the study or build exhibits."
}
Data Analysis & Endogeneity (smj-data-analysis)
When to trigger
- You have a performance regression with no endogeneity / reverse-causality treatment
- DID, IV, matching, or a selection model is chosen but not yet stress-tested
- Reviewers will ask "how do you know this is causal and not selection?"
- You need to plan the mechanism test and the robustness battery
The SMJ endogeneity mandate
Performance regressions with unaddressed endogeneity or reverse causality are the #1 SMJ rejection reason. Treat causal identification as a first-class part of the paper, not a footnote. The reviewer's mental model: firms that make this strategic choice are different in ways that also affect performance. You must close that door explicitly.
SMJ codifies this in Bettis, Gambardella, Helfat & Mitchell (2014), "Quantitative empirical analysis in strategic management," SMJ 35(7): 949–953: acknowledge endogeneity, make a good-faith effort to address it, and avoid data snooping / p-hacking. Report economic magnitudes, not just stars. SMJ will publish well-designed studies that report null results — so do not suppress a theory-relevant null.
Threat → tool map
| Threat | Primary tools |
|---|---|
| Self-selection into the strategic choice | IV, Heckman selection, PSM/CEM + DID, Rosenbaum bounds |
| Reverse causality / simultaneity | Exogenous shock + DID, lagged + Granger-style tests, IV |
| Unobserved time-invariant heterogeneity | Firm fixed effects (caveat: cannot fix time-varying confounds) |
| Omitted environmental confound | Industry-year FE, region FE, controls, falsification tests |
| Measurement error in X | IV, multiple indicators, sensitivity analysis |
Pick from the threat named in smj-methods; usually you will combine FE with one identification tool.
Design-specific execution
DID / natural experiment
- Test and show parallel pre-trends (event-study plot, not just a claim).
- If treatment timing is staggered, address heterogeneous-treatment-effect bias (Goodman-Bacon decomposition; Callaway–Sant'Anna or Sun–Abraham estimators) rather than naive two-way FE.
- Run placebo tests (fake treatment dates; unaffected units) and report effect dynamics.
Instrumental variables
- Report the first-stage F (weak-instrument concern below conventional thresholds → use weak-IV-robust inference).
- Make the exclusion argument in prose: why the instrument affects performance only through the strategic choice. Reviewers reject IVs whose exclusion is implausible.
- Report the reduced form and over-identification tests where applicable.
Matching (PSM / CEM) + DID
- Report covariate balance before/after; show common support.
- Matching handles selection on observables only; combine with DID and acknowledge residual selection on unobservables (bounds).
Heckman selection
- Justify the exclusion restriction in the selection equation (a variable affecting selection but not the outcome). A Heckman with no valid exclusion restriction is identified only off functional form — reviewers know this.
Mechanism & robustness
- Mechanism test: if you theorized a mediator, test it (prefer evidence beyond a Baron–Kenny mediation regression — e.g., moderation-of-process, subsample variation in the mechanism).
- Magnitude, not just stars: interpret the key effect in economic terms (e.g., "a 1 SD increase in X → a Y% change in performance"); SMJ cares about meaningful effects.
- Robustness battery (report, do not bury): alternative DVs; alternative samples (drop dominant industries/years); alternative estimators; clustering choices; prior performance; survivorship. Where feasible, show sensitivity to the identifying assumption (partial-identification / bounding).
- Inference: cluster standard errors at the level of treatment assignment (often firm); justify the choice.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. SMJ is strategy — firm-level panels where strategic choices are endogenous; foreground IV / DiD identification and the endogeneity-of-strategy objection.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Checklist
- The identifying threat is stated and the matching tool is deployed
- Reverse causality is addressed by design, not by lags alone
- Firm (and industry-year) fixed effects included where appropriate
- DID: parallel-trends evidence shown; staggered-timing bias addressed; placebos run
- IV: first-stage strength reported; exclusion argued in prose; reduced form shown
- Matching: balance + common support reported; unobservable selection acknowledged
- Heckman: valid exclusion restriction, not functional-form identification
- Mechanism tested, not just asserted
- Robustness across DV, sample, estimator, and clustering reported
- Standard errors clustered at the assignment level
Anti-patterns
- Cross-sectional correlations interpreted causally — an instant credibility loss at SMJ
- "We include fixed effects" treated as a complete endogeneity defense (FE miss time-varying confounds)
- IV with an exclusion restriction no reviewer would believe
- Heckman or PSM run mechanically with no defensible exclusion / balance
- Ignoring that firms self-select into the very strategic choice being studied
- Staggered DID with naive two-way FE and no heterogeneity correction
- A wall of robustness tables that never confronts the central threat
- Specification hunting until p < 0.05 (data snooping); reporting stars with no economic magnitude; suppressing a theory-relevant null — all discouraged by SMJ
Output format
【Identifying threat】selection | reverse causality | unobserved heterogeneity | omitted confound
【Estimator】FE + [IV | DID | matching | Heckman | ...]
【Identification evidence】[parallel trends / first-stage F / balance / exclusion argument]
【Placebo / falsification】[done?]
【Mechanism test】[what + result]
【Robustness】[DV alt, sample alt, estimator alt, clustering]
【Residual threat acknowledged】...
【Economic magnitude reported】yes / add
【Nulls reported honestly (no p-hacking)】yes
【Next step】smj-contribution-framing
Templates & resources
../../resources/external_tools.md— Stata/R/Python packages (reghdfe, ivreghdfe, csdid/did, psmatch2, rdrobust) and strategy data sources../../resources/official-source-map.md— SMJ p-hacking / null-results / endogeneity policy and the Bettis et al. (2014) editorial
Version History
- 1839142 Current 2026-07-05 14:28
