empirical-research plugins

by Bryce Wang

Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml). Data cleaning → variable construction → Table 1 → diagnostics → estimation (OLS / IV / DID / RDD / PSM / SCM / DML / Causal Forest) → robustness battery → mechanism / heterogeneity / mediation → publication-ready tables & figures. Also covers epidemiology (TMLE / IPTW / Mendelian randomization / survival) and ML-causal (DML / meta-learners / Dragonnet) modes. Every line is explicit and swappable — built for teaching, referee-level audit, and strict replication.

by Bryce Wang

Explicit 8-step empirical-analysis pipeline in the modern tidyverse + econometrics R stack (dplyr + haven + fixest + did + synthdid + rdrobust + MatchIt + WeightIt + grf + DoubleML + mediation + marginaleffects + modelsummary). All eight steps fit in a single .qmd; `quarto render` produces a unified PDF / HTML / Word reproducibility report. Also covers epidemiology (tmle / ltmle / MendelianRandomization / survival) and ML-causal (DoubleML / grf / policytree) modes. The Quarto-rendered reproducibility report is unique to R.

by Bryce Wang

Explicit 8-step empirical-analysis pipeline in the traditional Stata ecosystem (reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + synth + psmatch2 + teffects + esttab + coefplot). From use / import all the way to .tex / .rtf tables and .pdf figures, with the full modern DID toolkit (bacondecomp + honestdid + rwolf + ritest). Also covers epidemiology and ML-causal (ddml / crforest) modes. The first choice when a referee or co-author insists on a Stata replication pack.