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–Abraham, Borusyak–Jaravel–Spiess, de Chaisemartin–D'Haultfœuille); report a Goodman-Bacon decomposition to show the bias TWFE would induce.
• Show a clean event study with pre-period leads flat around zero; do not assert parallel trends, demonstrate it (and probe with Rambachan–Roth honest-DID where pre-trends are imperfect).
• Define the policy-relevant estimand (ATT on treated jurisdictions; weight by population/exposure if the policy lesson requires it).
• Cluster at the policy-assignment level (often state/jurisdiction); address few-cluster issues (wild-cluster bootstrap).

Path B: IV / instrumented policy exposure

• Strong first stage; with weak instruments use Anderson–Rubin / weak-IV-robust sets and report the effective F.
• Defend the exclusion restriction in institutions and theory, not just statistically; argue the instrument affects outcomes only through the policy channel.
• State the LATE complier population and whether it is the policy-relevant margin.

Path C: RDD / bunching (eligibility thresholds, tax/benefit schedules)

• RDD: McCrary / Cattaneo–Jansson–Ma density test; data-driven bandwidth; covariate smoothness at the cutoff; bias-corrected robust CIs (rdrobust).
• Bunching at kinks/notches in tax or benefit schedules: defend the counterfactual density and the structural elasticity it implies.
• Be explicit that the estimate is local to the threshold and argue its policy relevance.

Path D: RCT / field experiment of a program

• Pre-registration with a pre-analysis plan; report deviations. Detailed instructions / protocol included.
• Randomization balance; attrition (Lee bounds if differential); multiple-hypothesis adjustment; explicit estimand and a take-up / intent-to-treat vs. treatment-on-treated distinction.
• Tie the experimental effect to the cost of the program so a welfare reading is possible.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue it. Full map: execution-with-mcp. AEJ: Policy evaluates programs and reforms; the design must carry a policy-relevant magnitude, not just statistical significance.

1. detect_design → recommend → fit with as_handle=true → audit_result to list the checks the design still owes.
2. Staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result (the pre-trend test is low-power, Roth 2022).
3. IV: effective_f_test + an anderson_rubin_ci (valid under weak instruments), not a 2SLS t-stat alone.
4. RDD: rdrobust (bias-corrected) + rddensity / mccrary_test for manipulation.
5. OVB: oster_delta / sensemakr — how strong a confounder would have to be.

Report the economic magnitude; route the full battery to the appendix; keep every number reproducible. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough. If StatsPAI/Stata are not connected, adapt the vendored resources/code/ skeleton and flag any unverified number.

Checklist

• The policy variation is named and the identifying assumption stated in one sentence
• Design-appropriate diagnostics shown (pre-trends / density / first-stage F / balance)
• Modern heterogeneity-robust estimator used where TWFE would bias
• Estimand is the policy-relevant one (right population, right weighting)
• Inference clustered at the assignment level; few-cluster handled
• SEs reported (no asterisks); the causal claim never exceeds what the design supports

Anti-patterns

• TWFE on staggered policy rollout with no heterogeneity-bias discussion
• Asserting parallel trends instead of showing flat, precisely-estimated leads
• An exclusion restriction defended only by a significant first stage
• An RDD estimate generalized far from the cutoff without argument
• An RCT with no pre-registration, no attrition analysis, or no link to program cost
• Reporting significance with asterisks instead of standard errors

Referee pushback mapped to the fix

• "Staggered TWFE here is biased." → Re-estimate with Callaway–Sant'Anna / Sun–Abraham; show flat leads + Bacon decomposition.
• "Pre-trends look slightly off." → Honest-DID (Rambachan–Roth) bounds; show the conclusion survives plausible violations.
• "This is just the effect at the threshold." → State the local estimand; argue why the threshold population is policy-relevant or extrapolate cautiously.

Output format

【Design】DID / IV / RDD-bunching / RCT
【Policy variation】the reform/cutoff/rollout that identifies the effect
【Identifying assumption】one sentence + how it is defended
【Diagnostics shown】[pre-trends / density / first-stage F / balance + attrition]
【Estimand】policy-relevant population + weighting; inference/clustering
【What it does NOT identify】[...]
【Next step】aejpol-theory-model (welfare mapping) or aejpol-robustness