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Use when selecting, implementing, or stress-testing the causal identification strategy for an empirical economics manuscript — difference-in-differences (including staggered designs), instrumental variables (including weak-IV-robust inference), regression discontinuity, synthetic control, or shift-share / Bartik. Apply before writing the introduction or results.
Passed 19 of 22 test cases with the skill, and 11 of 22 without it.
and 8 more in the full report
8 fixed11 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am evaluating our empirical design for a project where US states adopted a minimum wage policy at different dates between 2010 and 2020. I want you to review our setup against modern econometric standards, recommend the appropriate design and diagnostic workflow, check for potential referee objections, and output a structured identification gate review along with the final handoff block outlining the strategy, estimators, required diagnostics, and next steps.
| Checked | Without skill | With skill |
|---|---|---|
| Recommends an estimator designed for staggered policy adoption rather than standard two-way fixed effects. | Passed | Passed |
| Specifies running a formal joint test of pre-period coefficients. | Missed | Passed |
| Includes the key-value line NEXT SKILL: aer-robustness in the handoff section. | Missed | Passed |
The instructions your model reads when the skill loads.
In AER-track empirical economics, identification is the paper. This skill routes among canonical designs, modern defaults, and referee-facing diagnostics.
If the design is fragile, return to aer-topic-selection; writing cannot save it.
Is treatment assignment plausibly random conditional on observables?
├── Yes, by design (RCT, lottery) → run the RCT analysis; register PAP via AEA RCT Registry
└── No → identification must come from variation
├── Sharp threshold in a running variable → RDD (sharp or fuzzy)
├── Discrete policy change in some units, not others, over time → DiD
│ ├── Single treatment date → canonical 2×2 DiD
│ └── Staggered adoption → Callaway-Sant'Anna or Borusyak-Jaravel-Spiess
├── Endogenous regressor + plausibly exogenous shifter → IV
│ ├── Shifter × pre-existing exposure shares → shift-share / Bartik
│ └── Single instrument → weak-IV-robust inference if F < 50
├── One treated unit / aggregate intervention → synthetic control
└── None of the above → reconsider the question
Use TWFE if and only if:
test cases passed, vs 11 / 22 without
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