/00.3-Full-empirical-analysis-skill_R

name: Full-empirical-analysis-skill-R
description: Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF 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). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".
triggers:
  - R empirical analysis
  - tidyverse econometrics workflow
  - reproducible R script
  - Quarto empirical pipeline
  - fixest feols feglm fepois
  - high-dimensional fixed effects R
  - clubSandwich cluster-robust
  - fwildclusterboot wild cluster bootstrap
  - ivreg AER 2SLS R
  - did att_gt Callaway SantAnna R
  - eventstudyr event study R
  - did_imputation Borusyak R
  - synthdid R package
  - bacondecomp R Goodman Bacon
  - HonestDiD R Rambachan Roth
  - rdrobust R
  - rddensity R
  - Synth gsynth R
  - MatchIt nearest neighbor R
  - WeightIt IPW propensity R
  - cobalt balance check R
  - ebal entropy balancing R
  - grf causal forest R
  - DoubleML R
  - mediation R Imai
  - marginaleffects R
  - gtsummary table 1
  - modelsummary publication table
  - kableExtra LaTeX
  - texreg stargazer
  - flextable Word
  - ggplot2 coefplot
  - iplot fixest
  - binsreg R
  - haven read_dta sav
  - janitor clean_names
  - naniar missing
  # Mode A — Epidemiology / public health
  - epidemiology pipeline R
  - public health causal inference R
  - target trial emulation R
  - g-formula R gfoRmula
  - IPTW marginal structural model R
  - WeightIt PSweight
  - tmle ltmle doubly robust
  - HAL-TMLE R
  - Mendelian randomization R
  - MendelianRandomization package
  - TwoSampleMR
  - MRPRESSO
  - MR-Egger weighted median R
  - STROBE TRIPOD reporting R
  - EValue sensitivity R
  - Kaplan-Meier AFT survival R
  - survival survminer flexsurv
  - 流行病学 R
  - 公共健康 R
  # Mode B — ML causal inference
  - ML causal inference R
  - DoubleML R
  - grf causal forest R
  - meta-learner S T X R DR R
  - causalweight R
  - bartCause bcf
  - Bayesian causal forest BCF R
  - CATE distribution R
  - policytree R
  - off-policy evaluation R
  - conformalInference cfcausal
  - conformal causal prediction R
  - fairmodels fairness audit
  - causal discovery PC NOTEARS R
  - pcalg bnlearn
  - 因果机器学习 R

Full Empirical Analysis — Classical R Workflow

This skill is the *canonical* 8-step pipeline an applied economist runs on every empirical paper, written in the **modern tidyverse + econometrics R 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` for causal mediation, `marginaleffects` for post-estimation, `modelsummary`/`kableExtra