Modern DID (csdid / Sun-Abraham / honest_did), synthetic control, RDD — the standard toolkit for government think-tanks, policy consulting, and PhD-level causal identification.
target audience：MPA / MPP students, government think-tank researchers, PhD candidates working on policy evaluation
China Family Panel Studies 2018 · Chinese General Social Survey 2021 · CSMAR Financial Statement Database · China Household Finance Survey 2019
Personal annual income · Family annual income · Urban / rural status · Employment status · Household consumption expenditure
All methods below run inside the wizard with verified Stata / Python / R templates.
| method | typical use |
|---|---|
csdid Callaway-Sant'Anna 2021 | 交错处理 DID 黄金标准 |
sun_abraham 事件研究 | 动态处理效应分解 |
honest_did 平行趋势敏感性 | Rambachan-Sant'Anna 2023 必备 |
合成控制 synth | 省级 / 城市级单一处理单元 |
RDD / fuzzy RDD | 政策门槛、行政边界识别 |
dml / pdslasso | 高维控制变量的双重机器学习 |
Upload your data, declare research roles (DV / IV / controls), and the wizard runs the matching templates and generates a Word report — coefficients, standard errors and p-values come from real CSVs, never synthesised text.