Causal Identification (qje-identification)

When to trigger

• The empirical core is OLS + controls with an undefended causal claim
• A DID uses two-way fixed effects (TWFE) on staggered timing without modern estimators
• An IV's first stage is weak or the exclusion restriction is unargued
• An RDD lacks a McCrary density test or bandwidth-robustness
• You are unsure your design clears the QJE causal bar

The QJE identification bar

QJE rewards credible identification serving a big question, not technique for its own sake. Editorial reality shapes the bar: five Harvard-based Editors (Barro, Katz, Nunn, Shleifer, Stantcheva as of 2024) desk-screen in roughly two weeks, so the identifying variation must be legible and convincing on a first read. QJE's own canon mixes clean experiments and quasi-experiments with reduced-form ambition — e.g., Chetty, Hendren, Kline & Saez, "Where is the Land of Opportunity?" (QJE 2014), and Chetty et al. on Project STAR kindergarten effects (QJE 2011). The credibility ranking editors and referees implicitly apply (strong → weaker):

1. RCT / field experiment with pre-registration and balance
2. Sharp/fuzzy RDD at a clean policy threshold
3. DID / event study off a credibly exogenous policy shock (with modern estimators)
4. IV with a strong first stage and a defended exclusion restriction
5. Matching / selection-on-observables — acceptable only as a complement, rarely as the spine

Note the QJE-specific twist: a novel, hard-to-assemble dataset answering a first-order question can carry a paper even when the design is reduced-form, because the journal prizes the question and the lesson (Akerlof's "Market for 'Lemons'", QJE 1970, is the patron saint of idea-first papers). It will not save a narrow within-field estimate.

Branch paths

Branch A: DID / event study

• Staggered adoption? You must move beyond TWFE. Use Callaway–Sant'Anna, Sun–Abraham, or de Chaisemartin–D'Haultfœuille; report a Goodman-Bacon decomposition to expose "forbidden" comparisons.
• Pre-trends: show a clean event-study plot with leads; pre-period coefficients near zero.
• Inference: cluster at the level of treatment assignment; consider wild-cluster bootstrap with few clusters.
• Placebo / permutation tests on treatment timing and on outcomes that should not move.

Branch B: IV

• First-stage F should be strong; with weak instruments use Anderson–Rubin or other weak-IV-robust confidence sets, not just t-stats.
• Exclusion restriction argued in three registers: economic theory, institutional detail, and falsification (effect absent where the channel is absent).
• Report the reduced form and the OLS alongside IV; discuss the LATE/compliers interpretation.
• Defend the instrument's own exogeneity — not just relevance.

Branch C: RDD

• McCrary / Cattaneo–Jansson–Ma density test for manipulation at the cutoff.
• Optimal bandwidth (Calonico–Cattaneo–Titiunik) plus at least three bandwidth-robustness checks; report bias-corrected CIs.
• Covariate smoothness / balance at the threshold; placebo cutoffs.
• For fuzzy RDD, report the first stage in the discontinuity.

Branch D: RCT / field experiment

• Pre-registration / pre-analysis plan referenced; report deviations.
• Randomization balance table; attrition analysis (Lee bounds if differential).
• Multiple-hypothesis adjustment across outcomes/subgroups.
• External validity discussed: what does the experimental population teach beyond itself?

Branch E: novel descriptive / measurement paper

• Is the data genuinely new and hard to assemble? (At QJE this is a real path, but only with first-order facts.)
• Are the new facts disciplined against measurement error and alternative explanations?
• Is there a clear conceptual lesson the facts deliver to all of economics, not one subfield?

Execution bridge (StatsPAI / Stata MCP)

A QJE identification claim should be estimated and audited, not just argued. Full map: shared-resources/empirical-methods/execution-with-mcp.md. QJE applied-micro instantiation:

1. detect_design → recommend → fit with as_handle=true, then audit_result to enumerate the checks the design still owes.
2. Staggered DiD: callaway_santanna / sun_abraham (never bare TWFE); show the bacon_decomposition weight you are correcting; honest_did_from_result (Rambachan–Roth) because 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 to state how strong a confounder must be.

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

Checklist

• Identifying variation named in one sentence and defended as exogenous
• Design-appropriate diagnostics done (pre-trends / density / first-stage / balance)
• Modern estimator used where TWFE would be biased (staggered DID)
• Inference matched to assignment level; few-cluster issues addressed
• Placebo / permutation / falsification tests reported
• LATE / external-validity interpretation stated explicitly
• The claim never exceeds what the design supports

Anti-patterns

• TWFE on staggered treatment with no discussion of heterogeneity bias
• IV that is "exogenous shock × lagged endogenous variable" with no exclusion argument
• "We argue treatment is as good as random" with no falsification evidence
• RDD reporting one bandwidth and hiding sensitivity
• A clean local design oversold as a global structural parameter with no broad lesson

Output format

【Design】RCT / RDD / DID / IV / descriptive / other
【Identifying variation】one sentence
【Diagnostics done】[pre-trends, density, first-stage F, balance, ...]
【Diagnostics missing】[...]
【Inference】clustering level + few-cluster handling
【Interpretation】LATE / ATT / external validity note
【Next step】qje-theory-model