eer-identification
GitHub针对EER期刊实证论文，在双重差分、IV、RDD或实验设计阶段进行可信度压力测试。确保因果识别逻辑清晰、假设明确且符合一般读者标准，提供具体分支的审计与执行建议。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eer-identification -g -y
SKILL.md
Frontmatter
{
"name": "eer-identification",
"description": "Use when the empirical causal-identification argument is the bottleneck for a European Economic Review (EER) manuscript — DiD\/event-study, IV, RDD, or experiment. Stress-tests the design to EER's general-interest credibility bar before exhibits are finalized; it does not build the theory model or the robustness battery."
}
Identification Strategy (eer-identification)
When to trigger
- A causal claim rests on OLS + controls, or TWFE on staggered timing
- An IV's exclusion restriction or first-stage strength is contested
- An RDD's continuity/manipulation assumptions are unexamined
- An experiment's estimand, balance, or pre-registration is unclear
- You are unsure the design clears EER's credibility bar for a general-interest readership
The EER identification bar
EER publishes broadly across empirical economics, so identification is judged on credibility legible to a general reader: the mapping from variation in the data to the causal object must be explicit, the key assumption stated, and the most obvious threat pre-empted. Because review is single-anonymized, the referee is often a methods expert in your exact design — modern, design-appropriate estimators and honest inference are expected. Report standard errors and confidence intervals (EER house style; do not lean on significance stars — see eer-tables-figures). Match the size of the causal claim to what the design supports.
Branch paths
Branch A: DiD / event study
- With staggered adoption, move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille); a TWFE coefficient on staggered timing must be defended against heterogeneity bias.
- Show a clean event-study with pre-treatment leads (flat, precisely estimated) and dynamic post effects.
- Report a Goodman-Bacon decomposition when using two-way fixed effects.
- State the parallel-trends assumption and a pre-trends / sensitivity argument (e.g., Rambachan–Roth honest DiD).
Branch B: IV
- Strong first stage (report the first-stage F / effective F); with weak instruments use Anderson–Rubin / weak-IV-robust sets.
- Defend the exclusion restriction in theory, institutions, and a falsification/placebo test.
- Be explicit about the LATE / complier interpretation; do not generalize beyond it.
Branch C: RDD
- Density/manipulation test (McCrary or Cattaneo–Jansson–Ma); covariate smoothness at the cutoff.
- Optimal bandwidth + bias-corrected CIs (Calonico–Cattaneo–Titiunik); show sensitivity to bandwidth.
- State the local nature of the estimate.
Branch D: Experiment / behavioral
- Pre-registration where applicable; report deviations; include instructions / survey transcripts.
- Randomization balance; attrition (Lee bounds if differential); multiple-hypothesis adjustment.
- State the estimand and external-validity scope.
Clustering at the level of treatment assignment; with few clusters use wild-cluster bootstrap. Pair this skill with
eer-robustnessfor the specification/sample battery.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. EER is a general economics field journal; the DiD/IV/RDD chain serves its applied lane.
detect_design→recommend→ fit withas_handle=true→audit_result.- Observational causal claims: staggered DiD (
callaway_santanna/sun_abraham+bacon_decomposition+honest_did_from_result); IV (effective_f_test+anderson_rubin_ci); RDD (rdrobust+mccrary_test). - Experiments: randomization-based inference +
romano_wolffor many-outcome control. - Sensitivity:
oster_delta/sensemakrfor observational claims.
Report the magnitude in interpretable units; route the full battery to the appendix. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Checklist
- Branch chosen; the variation-to-causal-object mapping stated in one sentence
- DiD: heterogeneity-robust estimator where TWFE would bias; flat pre-trends shown
- IV: first-stage strength reported; exclusion defended + falsification; LATE stated
- RDD: density test + bias-corrected CI + bandwidth sensitivity
- Experiment: pre-registered (if applicable); balance/attrition/MHT handled; estimand stated
- Inference: SEs/CIs reported, clustering at assignment level, few-cluster fix if needed
- Causal claim never exceeds what the design supports
Anti-patterns
- TWFE on staggered treatment with no heterogeneity-bias discussion
- An IV with an asserted-but-undefended exclusion restriction
- RDD with no manipulation test and a single hand-picked bandwidth
- An experiment with no pre-registration mention and no estimand
- Reporting significance with asterisks instead of SEs/CIs (against EER house style)
- Generalizing a LATE or a local RDD effect to a population it does not identify
Worked vignette (illustrative)
A migration paper uses a staggered visa-liberalization rollout. A weak version runs TWFE and reports a wage effect with stars. An EER version re-estimates with Callaway–Sant'Anna, shows flat leads and a dynamic post path, reports the effect as -1.4% local wages (s.e. 0.5, illustrative), runs Rambachan–Roth sensitivity, and states the estimand is the effect on incumbents in receiving regions — not a national average. The general-interest lesson (how labor supply shocks transmit to local wages) is named so a non-migration economist sees the point.
Output format
【Branch】DiD / IV / RDD / experiment
【Variation→object mapping】one sentence
【Key assumption】stated + the main threat pre-empted
【Design evidence】[pre-trends / first-stage F / density test / balance]
【Inference】SEs/CIs; clustering level; few-cluster fix?
【What it does NOT identify】[...]
【Next step】eer-theory-model (if a mechanism is needed) or eer-robustness
Version History
- 1839142 Current 2026-07-05 13:12
