本文へ移動
cccskills
無料GitHub で公開

aeja-tables-figures

Use when building or revising the exhibits of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the main causal result is legible in one table or figure and respects AEA house presentation norms. Formats exhibits; it does not establish the result (aeja-identification / aeja-robustness) or write the surrounding prose.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md6.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Tables & Figures (aeja-tables-figures)

When to trigger

  • The main result is settled and must be made readable at a glance
  • Tables are dense, over-decimaled, or bury the headline coefficient
  • An event-study / RD / first-stage plot needs to carry the identification visually
  • You are preparing exhibits for submission and want them AEA-house-style compliant

The AEJ: Applied exhibit bar

At AEJ: Applied the main causal estimate should be findable in seconds and every exhibit should earn its place. AEA house style permits significance stars but expects standard errors in parentheses, clear notes that make each exhibit self-contained, and clean figures over chartjunk. Lead with the design's signature visual — the event-study plot, the RD scatter, or the balance table — because at this journal the picture of the identification is half the persuasion.

ExhibitWhat it must showCommon failure
Main results tableheadline coefficient, SE in parentheses, N, controls indicated, dependent-var meantoo many columns; no SEs; over-precision
Balance table (RCT)baseline means by arm, differences, joint testmissing joint test; no attrition row
Event-study figureleads + lags, CIs, reference period, flat pre-trends visibleno CIs; ambiguous reference period
RD figurebinned scatter + fitted lines, bandwidth, densityoverfit polynomial; no density panel
First-stage / IV tablefirst-stage F, exclusion logic in notesweak first stage hidden
Robustness exhibitpoint-estimate stability across checksa starred wall with no map

Exhibit craft

  1. One table for the headline. Table 1 (or 2) should let a referee read the main causal estimate, its SE, and N without flipping pages.
  2. Standard errors always, stars optional. AEA permits stars, but SEs in parentheses are the load-bearing object; report the dependent-variable mean so magnitudes are interpretable.
  3. Self-contained notes. Each exhibit's note states sample, units, clustering level, controls, and what an asterisk (if used) means — a referee should not need the text to read the table.
  4. Figures carry identification. Event-study leads, RD continuity, and balance are more convincing as figures than as prose; make them publication-clean (vector output, readable fonts, CIs shown).
  5. Right precision. Two to three significant figures; do not report coefficients to five decimals.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: execution-with-mcp.

  • Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id — one variable definition, one set of numbers, body and appendix in sync.
  • Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
  • Every note names the estimator + clustering (from the result's diagnostics) and states the magnitude in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

  • Main causal estimate readable in one table: coefficient, SE in parentheses, N, dep-var mean, controls flagged
  • Standard errors reported everywhere; clustering level stated in notes; stars (if used) defined
  • Identification figure present (event-study with CIs / RD with density / balance with joint test)
  • Notes make every exhibit self-contained (sample, units, clustering, controls)
  • Figures are clean vector output, legible, no chartjunk, consistent scales
  • Precision sensible (2–3 sig figs); no redundant exhibits

Anti-patterns

  • A main results table with 12 columns where the headline coefficient is hard to find
  • Reporting stars or t-stats but omitting standard errors / clustering level
  • Over-precision (coefficients to 5 decimals) implying false accuracy
  • An event-study plot with no confidence intervals or unclear reference period
  • An RD figure with a high-order global polynomial that manufactures a jump
  • Exhibit notes that force the reader back to the text to interpret the table

Worked vignette (illustrative)

A draft's Table 4 has 12 columns sweeping every control combination, and the headline coefficient is buried in column 9 with only t-statistics shown. The AEJ: Applied fix: promote the preferred specification to a two-panel Table 2 — Panel A the main estimate (coefficient 3.1, s.e. 0.9 in parentheses, N, dependent-var mean 0.44), Panel B the same with the full controls — and move the sweep to the online appendix. Add Figure 1, the event-study with confidence intervals and a clearly marked reference period, so the identification is visible before the reader reaches the table. The result is now findable in seconds.

Referee pushback mapped to the exhibit fix

  • "I cannot find your main estimate." → One headline table with the coefficient, SE, N, and dep-var mean; everything else demoted to the appendix.
  • "Where are the standard errors / what is the clustering?" → SEs in parentheses everywhere; clustering level and (if used) star meaning stated in the self-contained note.
  • "This RD jump looks like an artifact of the polynomial." → Replace the global high-order fit with a local-linear binned scatter plus a density panel.

Output format

【Headline exhibit】one table/figure carrying the main estimate? [Y/N]
【Inference shown】SEs in parentheses + clustering level in notes? [Y/N]; stars defined if used
【Identification figure】event-study / RD / balance present with CIs? [Y/N]
【Self-contained notes】sample/units/clustering/controls in every note? [Y/N]
【Figure quality】vector, legible, no chartjunk? [Y/N]
【Next step】aeja-writing-style

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

brycewang-stanford のスキルをすべて見る

このスキルの問題を報告する