er-identification
Use when the empirical identification strategy is the bottleneck for an Economic-Research manuscript — quasi-experimental designs (DID, IV, RDD, DML, event study). Stress-tests the design against modern (2019-2024) estimators and reporting standards before drafting tables.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill er-identification --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 因果识别策略（er-identification） 配套代码：`resources/code/stata/03_did_modern.do`（DID）、`04_iv.do`（IV）、`05_rdd.do`（RDD）、`06_dml.do`（DML）。 ## 触发时机 - 实证主体仅有 OLS + 控制变量 - DID 用了 TWFE 但没回应近年异质性处理效应批评 - IV 第一阶段弱 / 工具变量内生性疑虑 - 准备用双重机器学习但不确定怎么报告 ## 设计优先级 《经济研究》编委的偏好排序（强 → 弱）： 1. **政策冲击 + DID（含 staggered / continuous treatment）** 2. **断点回归（清晰的政策门槛）** 3. **工具变量（强工具 + 排他性论证）** 4. **倾向得分匹配 + DID** 5. **合成控制法** 6. **双重机器学习 / 因果森林** 7. OLS + 严密内生性讨论（在结构估计 / 理论实证文章中可接受） > 该刊明确反对"唯定量倾向"：识别策略再漂亮，也要回到理论与中国制度问题。识别是手段，不是卖点。 ## 分支 A：交叠（多时点）DID —— ★ 最常见也最易被挑 交叠 DID **不能只报 TWFE**。标准流程四步： 1. **TWFE 基准**——读者熟悉的起点（但交叠下可能有偏）。 2. **Goodman-Bacon (2021) 分解**（`bacondecomp`）——展示"坏比较 / 负权重"问题。 3. **异质性稳健估计量做主结果**（下表任选其一为主，其余作稳健性）： | 估计量 | 论文 | Stata | R | |--------|------|-------|---| | group-time ATT | Callaway & Sant'Anna (2021) | `csdid` | `did::att_gt` | | 交互加权 IW | Sun & Abraham (2021) | `eventstudyinteract` | `fixest::sunab` | | 插补（最有效率） | Borusyak, Jaravel & Spiess (2024) | `did_imputation` | `didimputation` | | 两阶段 | Gardner (2022) | `did2s` | `did2s` | | 非二值/可逆处理 | de Chaisemartin & D'Haultfœuille (2020/24) | `did_multiplegt_dyn` | `DIDmultiplegtDYN` | ```stata * Callaway-Sant'Anna：gvar = 首次受处理年份，从不处理者 = 0 csdid
- 触发时机
- 设计优先级
- 分支 A：交叠（多时点）DID —— ★ 最常见也最易被挑
- 分支 B：IV —— 报告现代弱工具诊断
- 分支 C：RDD
- 分支 D：DML 双重机器学习
- 分支 E：结构估计 / 理论实证
- 标准误
- 执行桥（StatsPAI / Stata MCP）
- 必查清单
- 反模式
- 输出格式
What does the er-identification skill do?
Use when the empirical identification strategy is the bottleneck for an Economic-Research manuscript — quasi-experimental designs (DID, IV, RDD, DML, event study). Stress-tests the design against modern (2019-2024) estimators and reporting standards before drafting tables.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill er-identification --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.