Agent skill · Data & Analytics

aeja-identification

Use when the causal identification argument is the bottleneck for an American Economic Journal: Applied Economics (AEJ: Applied) manuscript — RCT, difference-in-differences/event study, regression discontinuity, IV, or shift-share. Stress-tests the data-to-causal-estimate mapping to the AEJ: Applied credibility bar before exhibits are finalized; it does not write the prose or build the package.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aeja-identification --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 7 KB
Bundled scripts: none
Path: AEJ-Applied-Economics-Skills/skills/aeja-identification/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Identification Strategy (aeja-identification) ## When to trigger - A causal claim rests on OLS + controls, or TWFE on staggered timing - An RCT's estimand, balance, or attrition handling is not pinned down - An RD's density, bandwidth, or covariate-smoothness defense is missing - An IV's first stage is weak or the exclusion restriction is asserted, not argued - You are unsure the design clears AEJ: Applied's credibility bar ## The AEJ: Applied identification bar AEJ: Applied is **identification-driven applied micro**: the **mapping from a source of variation to the causal estimand must be explicit, defended, and falsifiable**. Editors and referees here are unusually sophisticated about modern design pitfalls — staggered-DID bias, weak IV, RD manipulation, shift-share exogeneity. State the estimand, name the identifying assumption, show the diagnostic that could have failed but didn't, and keep the claim inside what the design supports. Inference must match the design (clustering at the assignment level; few-cluster corrections). ## Design paths ### Path A: RCT / field experiment (own data) - **Estimand stated** (ITT vs. LATE/TOT); randomization unit and stratification described.

What's inside
Steps it walks through
  1. When to trigger
  2. The AEJ: Applied identification bar
  3. Design paths
  4. Path A: RCT / field experiment (own data)
  5. Path B: Difference-in-differences / event study
  6. Path C: Regression discontinuity
  7. Path D: IV / shift-share
  8. Execution bridge (StatsPAI / Stata MCP)
  9. Checklist
  10. Anti-patterns
  11. Worked vignette (illustrative)
  12. Output format
More from Awesome-Journal-Skills
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About this skill
What does the aeja-identification skill do?

Use when the causal identification argument is the bottleneck for an American Economic Journal: Applied Economics (AEJ: Applied) manuscript — RCT, difference-in-differences/event study, regression discontinuity, IV, or shift-share. Stress-tests the data-to-causal-estimate mapping to the AEJ: Applied credibility bar before exhibits are finalized; it does not write the prose or build the package.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aeja-identification --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going