用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill robustness命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
| name | robustness |
| description | Checklist of empirical robustness tests for finance/economics papers |
Systematic checklist of robustness tests for empirical research. Use this to ensure comprehensive testing before submission.
| Test | Description | When to Use |
|---|---|---|
| Exclude outliers | Winsorize/trim at different levels (0.5%, 2%, 5%) | Always |
| Drop financial firms | Exclude SIC 6000-6999 | If not already excluded |
| Drop regulated industries | Exclude utilities, telecoms | Industry-specific effects |
| Different time periods | Split sample pre/post crisis, early/late | Results may be period-specific |
| Geographic subsamples | By region, state, country | External validity |
| Size subsamples | Small vs. large firms | Heterogeneous effects |
| Balanced panel | Require continuous observations | Survivorship concerns |
| Test | Description | When to Use |
|---|---|---|
| Different fixed effects | Firm, industry×year, state×year | Control for unobservables |
| Additional controls | Add variables referees might suggest | Omitted variable concerns |
| Drop controls | Verify not over-controlling | Mediator concerns |
| Different clustering | Firm, industry, state, two-way | Inference robustness |
| Different standard errors | Bootstrap, Newey-West, Driscoll-Kraay | Serial/cross-sectional correlation |
| Nonlinear specifications | Quadratic terms, splines | Linearity assumption |
| Log vs. level | Transform dependent variable | Skewed distributions |
| Test | Description | When to Use |
|---|---|---|
| Alternative dependent variable | Different proxy for same concept | Measurement concerns |
| Alternative treatment measure | Continuous vs. binary, different threshold | Treatment definition |
| Alternative control measures | Different proxies for size, leverage, etc. | Standard practice |
| Scaled differently | By assets, sales, employees | Scaling choice matters |
| Test | Description | When to Use |
|---|---|---|
| Placebo/Falsification | ||
| Placebo timing | Fake treatment 1-3 years before actual | DiD parallel trends |
| Placebo outcome | Effect on outcome that shouldn't be affected | Specificity of mechanism |
| Placebo treatment | Random assignment of treatment | Rule out spurious correlation |
| Pre-trends | ||
| Event study plot | Coefficient for each pre/post period | Visual parallel trends |
| Joint F-test | Test pre-period coefficients = 0 | Statistical parallel trends |
| Endogeneity | ||
| Instrumental variables | Find exogenous variation | Selection concerns |
| Heckman selection | Model selection explicitly | Sample selection |
| Propensity score matching | Match treated/control | Observable selection |
| Entropy balancing | Reweight to balance covariates | Covariate imbalance |
| Regression discontinuity | If threshold exists | Sharp identification |
| Test | Description | When to Use |
|---|---|---|
| Wild cluster bootstrap | Small number of clusters | <50 clusters |
| Randomization inference | Permutation-based p-values | Few treated units |
| Conley standard errors | Spatial correlation | Geographic data |
| Multiple hypothesis correction | Bonferroni, FDR | Many outcomes tested |
For difference-in-differences designs:
For instrumental variables:
At minimum, most papers should include:
For robustness tables:
Table X: Robustness Tests
Panel A: Alternative Samples
(1) Baseline
(2) Exclude financial firms
(3) Exclude 2008-2009
(4) Winsorize at 5%
Panel B: Alternative Specifications
(5) Add industry×year FE
(6) Control for firm age
(7) Cluster by industry
Panel C: Alternative Measures
(8) Alternative dependent variable
(9) Continuous treatment measure