aejpol-identification
Use when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Stress-tests the quasi-experimental policy-evaluation design to the AEJ: Policy bar before exhibits are finalized; it does not build the welfare mapping or write exhibits.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejpol-identification --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Identification — Credible Policy Evaluation (aejpol-identification) ## When to trigger - The causal effect of a policy rests on OLS + controls, or TWFE on staggered policy adoption - A reform / threshold / experiment exists but the design's assumptions are not pinned down - A referee questions whether the estimated effect is really *caused by the policy* - You are unsure the design clears AEJ: Policy's credible-causal-evidence bar ## The AEJ: Policy identification bar AEJ: Policy is an empirical policy journal: the **effect attributed to the policy must be credibly causal**, the **estimand must be the policy-relevant one**, and the design must survive the obvious confound that the policy was not random. The policy variation *is* the research design — name it explicitly (a reform date, an eligibility cutoff, a formula kink, a randomized rollout) and defend the assumption that makes it causal. Report **standard errors** (no significance asterisks; see `aejpol-tables-figures`) and make the design reproducible for the AEA Data Editor. ## Design paths ### Path A: DID / event study (reforms, staggered policy adoption) - With staggered adoption move beyond TWFE (Callaway–Sant'Anna, Sun–
- When to trigger
- The AEJ: Policy identification bar
- Design paths
- Path A: DID / event study (reforms, staggered policy adoption)
- Path B: IV / instrumented policy exposure
- Path C: RDD / bunching (eligibility thresholds, tax/benefit schedules)
- Path D: RCT / field experiment of a program
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Referee pushback mapped to the fix
- Output format
What does the aejpol-identification skill do?
Use when the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Stress-tests the quasi-experimental policy-evaluation design to the AEJ: Policy bar before exhibits are finalized; it does not build the welfare mapping or write exhibits.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejpol-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.