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Cre Identification

ASecurity

Use when the empirical identification strategy is the bottleneck for a 《中国农村经济》 manuscript — micro-household / village-level quasi-experimental designs (DID, IV, RDD, PSM). Stress-tests the design and the rural-specific endogeneity before drafting tables.

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Added 6/4/2026
ai-agentsgo

Security Analysis

A100/100

Scanned 6/4/2026

Install to Claude Code

$npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill cre-identification --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
name: cre-identification
description: Use when the empirical identification strategy is the bottleneck for a 《中国农村经济》 manuscript — micro-household / village-level quasi-experimental designs (DID, IV, RDD, PSM). Stress-tests the design and the rural-specific endogeneity before drafting tables.
---

# 因果识别策略(cre-identification)

## 触发时机

- 实证主体仅有描述统计 + OLS + 控制变量
- 自变量是农户的**自选择行为**(入合作社、外出务工、采用新技术、参与流转),但没处理自选择内生性
- DID 用了 TWFE 但没回应近年异质性处理批评(Goodman-Bacon, de Chaisemartin, Sun-Abraham, Callaway-Sant'Anna)
- IV 第一阶段 F 弱 / 工具变量排他性疑虑
- PSM 只做了匹配但没讨论"不可观测的选择性"

## 设计优先级

《中国农村经济》编委对农村微观研究的偏好排序(强 → 弱):

1. **农村政策冲击 + DID(含 staggered / continuous treatment)**——如某项试点改革、确权、补贴政策的分批推行
2. **断点回归**——清晰的政策门槛(如贫困县划定线、补贴资格线、年龄门槛)
3. **工具变量**——强工具 + 排他性论证(处理农户自选择的核心武器)
4. **倾向得分匹配 + DID(PSM-DID)**——农村横截面 / 短面板的常见组合
5. **合成控制法**——村级 / 县级政策评估
6. OLS + 严密的自选择讨论(仅在有强外生性论证或结构 / 理论实证时可接受)

## 农村数据的内生性专项

农村微观研究最常见的内生性来源,审稿人必查:

- **自选择进入处理**:是否更"能干 / 风险偏好 / 资源禀赋好"的农户才入社 / 流转 / 务工?
- **反向因果**:是收入高才参与,还是参与带来收入?
- **测量误差**:农户自报的收入 / 土地面积 / 用工常有偏差
- **遗漏的村级 / 家庭固定特征**:村庄区位、宗族网络、家庭社会资本

针对性策略至少给出一条:固定效应 + IV / 准实验冲击 / 匹配 + 安慰剂 / 双稳健(Heckman 仅作辅助,不能单独立住识别)。

## 分支路径

### 分支 A:DID

- 是否 staggered?→ 必须用 Goodman-Bacon 分解 + Callaway-Sant'Anna 或 Sun-Abraham
- 平行趋势检验:事件研究图必须画
- 安慰剂:随机分配处理村 / 处理户 500–1000 次
- 是否报告 Bacon 分解的"坏比较"权重?

### 分支 B:IV

- 第一阶段 F **必须 ≥ 10**(弱工具 → 用 Anderson-Rubin 或 weak-IV-robust CI)
- 排他性论证至少需要 3 段:理论 / 制度 / 安慰剂;说明工具只通过处理变量影响农户结果
- 农村常用工具(地理 / 历史 / 政策外生)的内生性也要论证,不能"看起来外生就行"
- 是否报告了 reduced form?

### 分支 C:RDD

- 是否做了 McCrary / 密度检验(防止农户在门槛附近操纵,如人为划线进贫困户)?
- 带宽:最优带宽(Calonico-Cattaneo-Titiunik)+ 至少 3 个带宽稳健性
- 协变量平滑性检验

### 分支 D:PSM / PSM-DID

- 报告匹配前后的协变量平衡性检验
- 报告共同支撑域(common support)
- **明确讨论"不可观测选择性"**——PSM 只能处理可观测变量,需补 IV 或敏感性分析(Rosenbaum bounds)
- 优先 PSM-DID 而非纯横截面 PSM

### 分支 E:结构估计 / 理论实证

- 农户决策模型的微观基础是否清晰?
- 识别假设是否明确列出?
- 是否提供反事实(如政策模拟)?

## 必查清单

- [ ] 平行趋势 / 平滑性 / 弱工具 / 匹配平衡 检验都做了
- [ ] 农户自选择内生性有明确处理(不是一句"我们假设外生")
- [ ] 安慰剂检验做了(处理时点随机 / 处理村或户随机)
- [ ] 标准误聚类层次合理(农户 / 村 / 县 / 政策推行层级)
- [ ] 是否回应了"被处理农户预期"问题(如预期确权而提前调整行为)

## 反模式

- 纯描述性统计 + OLS 就下因果结论
- 自变量是农户自选择行为(入社 / 务工 / 流转)却不处理自选择
- TWFE + staggered 但不讨论异质性处理偏误
- PSM 只报匹配结果,回避"不可观测选择性"
- IV 用"村级历史变量"但不论证它不通过其他渠道影响当代农户结果
- RDD 用了门槛但不做密度检验(农户可能操纵分组)

## 输出格式

```
【识别策略】DID / IV / RDD / PSM-DID / 结构估计 / 其他
【自选择处理】方式:[...]
【已完成检验】[平行趋势, 安慰剂, 弱工具, 匹配平衡, ...]
【缺失检验】[...]
【聚类层次】农户 / 村 / 县 / ...
【下一步】cre-mechanism
```

Attribution

brycewang-stanfordbrycewang-stanford
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