00.3-Full-empirical-analysis-skill_R

Full Empirical Analysis Skill for Claude Code — R edition

This folder is a Claude Code Skill that teaches Claude (or any compatible agent harness) how to drive a complete empirical analysis end-to-end in R, using the modern tidyverse + econometrics ecosystem: dplyr / tidyr / haven for data, fixest as the panel/IV/DID workhorse, did / bacondecomp / HonestDiD for modern DID, rdrobust / rddensity for RD, Synth / gsynth / synthdid for synthetic control, MatchIt / WeightIt / cobalt / ebal for matching, grf / DoubleML for ML causal, mediation / lavaan for mediation, marginaleffects for post-estimation, modelsummary / kableExtra / gt for publication tables, ggplot2 / iplot / binsreg for figures.

The skill covers three domain modes that share the same 8-step scaffolding:

• Default — Applied Econ (AER / QJE / AEJ). The canonical 8-step pipeline: import / cleaning → variable construction → Table 1 → diagnostic tests → baseline modeling (feols HDFE / IV / DID / RD / SCM / matching / DML) → robustness gauntlet → mechanism + heterogeneity → publication-ready Word / Excel / LaTeX bundle.
• Mode A — Epidemiology / public health (§A). Target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via WeightIt / gfoRmula / tmle / ltmle, Mendelian randomization (IVW / Egger / weighted median) via MendelianRandomization / TwoSampleMR / MRPRESSO, KM / Cox / AFT / RMST survival via survival / survminer / flexsurv, E-value sensitivity via EValue, principal stratification — under STROBE / TRIPOD-AI reporting conventions.
• Mode B — ML causal inference (§B). DML via DoubleML, S/T/X/R/DR meta-learners via causalweight / grf, causal forest via grf::causal_forest, BART / Bayesian causal forest via bartCause / bcf, matrix completion via MCPanel, CATE distribution + policy tree via policytree + off-policy evaluation, conformal causal via conformalInference / cfcausal, fairness audit via fairmodels, DAG learning via pcalg / bnlearn / LLM-assisted.

All three modes reuse the same Step 1–4 (cleaning → Table 1 → diagnostics) and Step 8 (publication tables / figures) scaffolding — switching modes only changes which Step-5 estimator family you reach for.

Philosophy

This is the R counterpart to the four-skill family in this repo:

Skill	Language / Stack
00-StatsPAI_skill	Python — agent-native one-import DSL (import statspai as sp)
00.1-Full-empirical-analysis-skill	Python — explicit traditional stack (pandas + statsmodels + linearmodels + pyfixest + …)
00.2-Full-empirical-analysis-skill_Stata	Stata — explicit .do pipeline (reghdfe + ivreg2 + csdid + …)
00.3-Full-empirical-analysis-skill_R (this skill)	R — tidyverse + fixest pipeline (feols + did + grf + modelsummary + …)

Same 8-step pipeline, four ecosystems. All four coexist; pick by your audience or workflow:

• Need a Quarto / R Markdown reproducible report ⇒ this skill
• Reviewer expects Stata .do files ⇒ 00.2
• Want explicit Python control ⇒ 00.1
• Want a one-import DSL with self-describing API ⇒ 00

Install

# Option 1: copy
cp -r 00.3-Full-empirical-analysis-skill_R \
      ~/.claude/skills/Full-empirical-analysis-skill_R

# Option 2: symlink
ln -s "$(pwd)/00.3-Full-empirical-analysis-skill_R" \
      ~/.claude/skills/Full-empirical-analysis-skill_R

Install the R packages (run once on a fresh R; consider renv::init() for project-level locking):

install.packages(c(
  # Data
  "tidyverse", "haven", "readxl", "data.table", "janitor",
  "naniar", "VIM", "mice", "validate", "assertr", "DescTools",
  # Description / tables
  "gtsummary", "tableone", "modelsummary", "kableExtra", "gt",
  "stargazer", "texreg", "flextable", "psych", "summarytools",
  "ggcorrplot", "corrplot",
  # Tests
  "lmtest", "sandwich", "car", "tseries", "urca", "plm",
  "clubSandwich", "fwildclusterboot", "skedastic",
  # Modeling — workhorses
  "fixest",                                       # primary
  "AER", "ivreg", "ivmodel",                      # IV
  # Modern DID
  "did", "didimputation", "synthdid",
  "bacondecomp", "HonestDiD", "DIDmultiplegtDYN",
  # RD
  "rdrobust", "rddensity", "rdmulti", "rdlocrand",
  # Synthetic control
  "Synth", "gsynth", "tidysynth",
  # Matching / weighting
  "MatchIt", "WeightIt", "cobalt", "ebal",
  # ML causal
  "grf", "DoubleML", "mlr3", "mlr3learners", "ranger",
  # Mediation / SEM
  "mediation", "lavaan", "semTools",
  # Robustness / inference
  "robomit", "ri2", "randomizr", "boot", "multcomp",
  # Margins / post-estimation
  "marginaleffects",
  # Plotting
  "ggplot2", "ggpubr", "cowplot", "patchwork", "ggdist",
  "binsreg", "ggrepel", "showtext",
  # Survival / quantile
  "quantreg", "survival",
  # Quarto rendering
  "quarto", "knitr", "rmarkdown", "broom"
))

Activate

Triggers: "run a full empirical analysis in R", "feols with two-way FE", "Callaway-Sant'Anna in R", "MatchIt nearest neighbor", "modelsummary to LaTeX", "plot_slopes for marginal effects", "gtsummary Table 1", "binsreg in R", "bacondecomp R", "HonestDiD R", "causal forest grf", "mediation Imai", etc. Full list in the triggers: field of SKILL.md.

Scope

In scope — the canonical 8-step R pipeline (v2 adds Step −1 / Step 0 / Step 2.5 / Step 3.5 sub-stages mirroring the StatsPAI reference skill):

Step −1 Pre-Analysis Plan         pwr / WebPower / DeclareDesign → pap.json
Step 0  Sample log + data contract sample_log + 5 stopifnot asserts → JSON
Step 1  Data import & cleaning   read_dta/read_csv/janitor/naniar/validate/assertr
Step 2  Variable construction    dplyr mutate/across/Winsorize/scale/lag/lead
Step 2.5 Empirical strategy       equation × ID assumption × estimator → strategy.md
Step 3  Descriptive statistics   gtsummary/datasummary_balance/cor_pmat/ggdist
Step 3.5 Identification graphics  iplot/binsreg/rdplot/cobalt::love.plot/Synth
Step 4  Diagnostic tests         shapiro/bptest/dwtest/vif/adf/kpss/Hausman
Step 5  Baseline modeling        feols/ivreg/att_gt/sunab/synthdid/MatchIt/grf
        Patterns A–H              progressive ctrls / horse race / multi-Y / IV triplet / ...
Step 6  Robustness battery       fwildclusterboot/ri2/bacondecomp/HonestDiD/robomit
        + Pattern H Master Table A1 + spec curve (specr) + sensitivity dashboard
Step 7  Further analysis         marginaleffects/mediation/lavaan/grf
Step 8  Tables & figures         modelsummary/iplot/ggplot2/cowplot/Quarto
        + reproducibility stamp (artifacts/result.json)

Out of scope — Bayesian R workflows (brms/rstan — see 23-Learning-Bayesian-Statistics-baygent-skills), agent-native one-import DSLs (00), and paper drafting (LaTeX prose). This skill ends at Step 8 with .tex / .docx tables and .pdf figures ready for the manuscript.

Files

• SKILL.md — frontmatter + agent playbook (8-step workflow + library cheat-sheet + common mistakes + project skeleton + Quarto pointer)
• README.md — this file
• references/ — deep per-step references, loaded on demand
  • 01-data-cleaning.md
  • 02-data-transformation.md
  • 03-descriptive-stats.md
  • 04-statistical-tests.md
  • 05-modeling.md
  • 06-robustness.md
  • 07-further-analysis.md
  • 08-tables-plots.md

---

R 完整实证分析技能（中文）

本文件夹是一份 Claude Code Skill，教 Claude（或任何兼容的 agent 运行时）端到端地用 R 完成一次实证分析，使用现代 tidyverse + 计量经济学 R 生态：dplyr/tidyr/haven 处理数据，fixest 作为面板 /IV/DID 主力，did/bacondecomp/HonestDiD 处理现代 DID，rdrobust /rddensity 处理 RD，Synth/gsynth/synthdid 处理合成控制， MatchIt/WeightIt/cobalt/ebal 处理匹配，grf/DoubleML 处理 ML 因果，mediation/lavaan 处理中介，marginaleffects 处理 后估计，modelsummary/kableExtra/gt 出版级表格，ggplot2/ iplot/binsreg 出图。

本 skill 覆盖三种领域模式，共用同一套 8 步骨架（清洗 / Table 1 / 诊断 / 出表）：

• 默认 — 应用经济学（AER / QJE / AEJ）。8 步流程：导入清洗 → 变量构造 → Table 1 → 诊断检验 → 基准建模（feols HDFE / IV / DID / RD / SCM / 匹配 / DML）→ 稳健 gauntlet → 机制 + 异质性 → 论文级 Word / Excel / LaTeX 三件套。
• 模式 A — 流行病学 / 公共健康（§A）。target-trial emulation， WeightIt / gfoRmula / tmle / ltmle 跑 IPTW + g-formula
  • TMLE 双稳健三件套，MendelianRandomization / TwoSampleMR / MRPRESSO 做孟德尔随机化（IVW / Egger / 加权中位数 / 异质性 outlier 鲁棒），survival / survminer / flexsurv 做 KM / Cox / AFT / RMST 生存分析，EValue 做 E-value 敏感性，principal stratification——按 STROBE / TRIPOD-AI 报告规范输出。
• 模式 B — 因果机器学习（§B）。DoubleML 跑 DML， causalweight / grf 跑 S/T/X/R/DR meta-learner， grf::causal_forest 跑因果森林，bartCause / bcf 跑 BART / Bayesian causal forest，MCPanel 做矩阵补全，CATE 分布 + policytree 策略树 + off-policy 评估，conformalInference / cfcausal 做 conformal causal 预测区间，fairmodels 做 fairness audit，pcalg / bnlearn / LLM 辅助做 DAG 学习。

三种模式共用同一套 Step 1–4（清洗 / Table 1 / 诊断）和 Step 8 （出表 / 出图）骨架——切换模式只换 Step-5 估计器组合。

哲学

本 skill 是仓库中四联 skill 的 R 版本：

Skill	语言 / 生态
00-StatsPAI_skill	Python — agent-native 一键 DSL（import statspai as sp）
00.1-Full-empirical-analysis-skill	Python — 显式传统生态
00.2-Full-empirical-analysis-skill_Stata	Stata — 显式 .do pipeline
00.3-Full-empirical-analysis-skill_R（本 skill）	R — tidyverse + fixest pipeline

同一 8 步流程、四种生态实现，并行收录、互不替代。按场景选：

• 要 Quarto / R Markdown 一体化复现报告 ⇒ 本 skill
• 审稿人 / 合作者只接受 Stata ⇒ 00.2
• Python 显式逐行控制 ⇒ 00.1
• 一键 DSL + 自描述 API ⇒ 00

安装

英文区已给出 install.packages(...) 大清单，复制粘贴一次跑完即可。 推荐用 renv::init() 在项目级别锁定包版本。

激活

触发词包括："用 R 跑一次完整实证分析"、"feols 双向固定效应"、 "Callaway-Sant'Anna R 版"、"MatchIt 最近邻匹配"、"modelsummary 导出 LaTeX"、"plot_slopes 边际效应图"、"gtsummary Table 1"、 "binsreg R 版"、"bacondecomp"、"HonestDiD"、"grf 因果森林"、 "Imai 中介" 等。完整列表见 SKILL.md 中的 triggers:。

覆盖范围

覆盖 —— R 经典 8 步 pipeline（v2 对齐 StatsPAI 参考 skill，新增 Step −1 / Step 0 / Step 2.5 / Step 3.5 子阶段）：

Step −1 预分析计划（PAP）   pwr / WebPower / DeclareDesign → pap.json
Step 0  样本日志 + 数据契约 sample_log + 5 项 stopifnot → JSON
Step 1  数据导入 / 清洗     read_dta/read_csv/janitor/naniar/validate/assertr
Step 2  变量构造           dplyr mutate/across/Winsorize/scale/lag/lead
Step 2.5 实证策略           方程式 × 识别假设 × 估计器 → strategy.md
Step 3  描述统计           gtsummary/datasummary_balance/cor_pmat/ggdist
Step 3.5 识别图            iplot / binsreg / rdplot / cobalt::love.plot / Synth
Step 4  诊断检验           shapiro/bptest/dwtest/vif/adf/kpss/Hausman
Step 5  基准建模           feols/ivreg/att_gt/sunab/synthdid/MatchIt/grf
        八种 regtable 模式 A–H  渐进控制 / 设计赛马 / 多 Y / IV 三联 / ...
Step 6  稳健性电池         fwildclusterboot/ri2/bacondecomp/HonestDiD/robomit
        + Pattern H 稳健性主表 A1 + 规范曲线（specr）+ 敏感性面板
Step 7  进一步分析         marginaleffects/mediation/lavaan/grf
Step 8  表与图             modelsummary/iplot/ggplot2/cowplot/Quarto
        + 复现戳（artifacts/result.json）

不覆盖 —— Bayesian R 工作流（brms/rstan，见 23-Learning-Bayesian-Statistics-baygent-skills）、 agent-native 一键 DSL（见 00）、以及正文撰写（LaTeX 文字）。本 skill 在 Step 8 结束，交付 .tex / .docx 表和 .pdf 图。

文件

• SKILL.md — frontmatter + 完整 agent 操作手册（8 步流程 + 选包速查表 + 常见坑 + 项目骨架 + Quarto 入口）
• README.md — 本文件
• references/ — 每步的深层参考，按需加载
  • 01-data-cleaning.md
  • 02-data-transformation.md
  • 03-descriptive-stats.md
  • 04-statistical-tests.md
  • 05-modeling.md
  • 06-robustness.md
  • 07-further-analysis.md
  • 08-tables-plots.md