Agent skill · Design & Presentation

jpam-research-design

Use when defending the causal identification of a Journal of Policy Analysis and Management (JPAM) manuscript — RCTs, difference-in-differences / event study, regression discontinuity / kink, instrumental variables, and synthetic control for policy and program evaluation. Strengthens the design and its assumptions; it does not write estimation code.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpam-research-design --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: Journal-of-Policy-Analysis-and-Management-Skills/skills/jpam-research-design/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

# Research Design & Identification (jpam-research-design) Credible identification is JPAM's **core bar**. The journal evaluates the effects of real policies and programs, so the design must connect the theory of change (`jpam-theory-building`) to evidence a policymaker can trust. State the **estimand**, the **assumptions** that license a causal reading, and **how each is defended** — then rule out the single strongest rival explanation. Selection-on- observables alone rarely clears the bar. ## When to trigger - Specifying or defending identification for a policy evaluation - A reviewer questioned causal claims, parallel trends, the instrument, the discontinuity, or confounding - Choosing among RCT / DiD / RD / IV / synthetic control for a given policy variation - Preparing a pre-analysis plan for a prospective program evaluation ## Design menu (match to the policy variation) - **RCT / field experiment.** The gold standard where feasible. Report randomization unit, balance, power/MDE, take-up, attrition, and ITT vs. TOT/LATE. Pre-register primary outcomes and subgroups. - **Difference-in-differences / event study.** For staggered policy adoption use **heterogeneity-robust estimators

What's inside
Steps it walks through
  1. When to trigger
  2. Design menu (match to the policy variation)
  3. Inference & policy-evaluation standards
  4. The adjudication test (JPAM-specific)
  5. Execution bridge (StatsPAI / Stata MCP)
  6. Checklist
  7. Anti-patterns
  8. Calibration anchors (hedged)
  9. Worked micro-example (illustrative)
  10. Output format
  11. Supplementary resources
More from Awesome-Journal-Skills
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About this skill
What does the jpam-research-design skill do?

Use when defending the causal identification of a Journal of Policy Analysis and Management (JPAM) manuscript — RCTs, difference-in-differences / event study, regression discontinuity / kink, instrumental variables, and synthetic control for policy and program evaluation. Strengthens the design and its assumptions; it does not write estimation code.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpam-research-design --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