Empirical paper analysis

Analyzes empirical law and economics papers by systematically evaluating problems, empirical challenges, identification strategies, key findings, and academi...

Empirical Paper Analysis Skill

Skill Description

This skill enables Claude Code to deeply analyze empirical research papers, following a structured framework: Problem Statement → Core Empirical Challenges → Identification Strategy → Key Findings → Academic Contribution.

Target User

Researchers in law and economics who regularly read and analyze empirical papers in law and economics, especially with quantitative methods (econometrics, machine learning, NLP, etc.).

Input Requirements

• PDF file of an empirical research paper
• Publication information (Authors, Journal, Date, etc)

Analysis Framework

1. 问题的提出 (Problem Statement)

Objective: Identify the core research question and its motivation.

Analysis Points:

• What is the primary research question? / What problem or phenomenon is being studied?
• Why is this question important (policy relevance, theoretical gap, methodological innovation, practical value)?
• What is the economic/legal intuition behind the research design?

2. 实证研究的核心难题 (Core Empirical Challenges)

Objective: Identify the key methodological obstacles that make causal inference difficult.

Common Challenges to Look For:

• Selection bias: Observed vs unobserved outcomes (e.g., selective labels problem)
• Omitted variable bias: Unobserved confounders (e.g., judges' private information)
• Endogeneity: Reverse causality or simultaneity
• Measurement error: How to quantify abstract concepts (e.g., legal ideas, judicial attitudes)
• External validity: Generalizability concerns
• Data limitations: Missing counterfactuals, truncated samples, etc.

Output Format: For each challenge:

• Clearly state the problem
• Explain why it matters for causal inference
• Use examples/tables to illustrate if helpful

3. 识别策略与方法设计 (Identification Strategy & Research Design)

Objective: Explain how the paper solves the empirical challenges.

Key Elements:

• Identification strategy: Natural experiment, IV, RD, DID, matching, ML+causal inference hybrid
• Data source: Dataset description, sample selection, time period
• Empirical specification: Main regression model, key variables
• Robustness checks: Alternative specifications, placebo tests, sensitivity analysis
• Novel methodological contributions: Any innovative techniques?

Critical Analysis:

• Are the identification assumptions plausible?
• Are there remaining threats to validity?
• How convincing is the causal interpretation?

4. 重要发现与结论 (Key Findings & Conclusions)

Objective: Summarize the main empirical results and their interpretation.

Structure:

• Main findings (with magnitude/significance)
• Robustness of results
• Heterogeneous effects (if any)
• Economic/legal interpretation
• Policy implications

Format:

• Use bullet points for clarity
• Include key numbers (effect sizes, significance levels)
• Reference important tables/figures

5. 学术价值 (Academic Contribution)

Objective: Evaluate the paper's broader significance.

Dimensions:

• Methodological innovation: New identification strategies, measurement techniques
• Theoretical contribution: New insights about legal/judicial behavior, institutional design
• Policy relevance: Implications for legal reform, judicial training, algorithm adoption
• Interdisciplinary impact: Bridges law, economics, computer science
• Future research: Opens new questions or directions

Output Format

Generate a structured markdown document following this template:

markdown

# [Paper Title]

**Authors:** [List]
**Journal:** [Name, Year]
**DOI/Link:** [If available]

## 问题的提出

[Analysis following framework above]

## 实证研究的核心难题

### 难题一：[Name]
[Explanation]

### 难题二：[Name]
[Explanation]

## 识别策略与方法设计

### 数据来源
[Description]

### 识别策略
[Core identification approach]

### 方法设计
[Technical details]

## 重要发现与结论

- **发现一：** [Finding with magnitude]
- **发现二：** [Finding with magnitude]
- **政策含义：** [Implications]

## 学术价值

- **方法论贡献：** [Innovation]
- **理论贡献：** [Insights]
- **政策相关性：** [Relevance]

Special Instructions

1. Academic Tone: Use precise academic language appropriate for PhD-level analysis. Assume familiarity with econometric concepts (DID, IV, RDD, etc.) and ML methods (GBDT, NLP, embeddings).

2. Bilingual Output: Primary language is Chinese (as shown in the examples), but technical terms can be included in parentheses with English abbreviation when first introduced.

3. Mathematical Rigor: Don't shy away from mathematical notation when describing models or identification strategies. For example:

   • Regression specifications: $Y_i = \beta_0 + \beta_1 Treatment_i + X_i'\gamma + \epsilon_i$
   • DID: $Y_{ijt} = \alpha + \beta(Post_t \times Treat_j) + \delta_j + \lambda_t + \varepsilon_{ijt}$

4. Critical Thinking: Don't just summarize—analyze. Question assumptions, evaluate identification strength, consider alternative explanations.

5. Tables/Figures: When referencing tables or figures from the paper:

   • Describe what they show conceptually
   • Highlight the most important results
   • Don't try to reproduce full tables in text

6. Scope: Focus on the five core sections. Don't add unnecessary sections.

Example Workflow

1. Read the entire paper to understand the research question and context
2. Extract the empirical strategy - pay special attention to identification sections
3. Identify the key challenges the authors face
4. Trace how they solve each challenge methodologically
5. Synthesize the findings with appropriate interpretation
6. Evaluate the contribution in context of the literature