Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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Use when placing the contribution against the policy-evaluation literature and sharpening it as a policy answer for an AEJ: Economic Policy manuscript. Stakes the contribution and the policy-relevant "so what"; it does not write the full introduction or design the identification.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
At AEJ: Policy the contribution is a better answer to a policy question, not a new estimate for its own sake. Position the paper on three axes and make the policy reading the headline:
aejpol-theory-model).Author–year throughout; cite the canonical methods papers behind your design (e.g., modern DID, RDD) and the key policy-evaluation papers on this and adjacent policies — referees expect you to know the policy literature, not only the econometrics.
[1][2] citations instead of AEA author–yearA draft on a carbon tax says "we estimate the emissions response." Referees note three prior estimates exist. Repositioned: the prior estimates ignore the revenue-recycling margin; this paper recovers the response and the incidence of the recycled revenue, so it can state the net welfare cost per ton abated under alternative recycling rules (illustrative) — a cost-benefit advance no prior estimate delivered. The contribution is now a policy answer, not a fourth elasticity.
A strong AEJ: Policy intro states the contribution as up to three numbered moves, at least one of them a welfare advance: (1) credibility — "we exploit [variation] that prior work could not, removing [confound]"; (2) magnitude / external validity — "we provide the policy-relevant estimate for [population/scale] where prior estimates do not travel"; (3) welfare — "we convert the effect into [MVPF / cost-per-outcome / incidence], the object a policymaker needs." Keep each to one sentence and make the welfare bullet the one a reader remembers.
【Open policy question / gap】what a decision-maker still does not know
【Closest prior work】1–3 papers + the specific limitation you address
【Contribution bullets】(a) credibility / (b) magnitude / (c) welfare-cost-benefit — ≥1 must be welfare
【Policy "so what"】one sentence a non-specialist policymaker would grasp
【Next step】aejpol-identification (design) or aejpol-theory-model (welfare mapping)
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
中文简介正在准备中。当前显示原文说明。
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
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Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
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Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
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Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
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Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
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