Skill: zhouziyue233/empirical-paper-analysis-skill (version 1.0.0)

# 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


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