Research Ideation - 研究构思与问题生成

--- name: research-ideation description: Generate structured research questions, testable hypotheses, and empirical strategies from a topic or dataset argument-hint: "[topic, phenomenon, or dataset description]" allowed-tools: ["Read", "Grep", "Glob", "Write"] --- # Research Ideation Generate structured research questions, testable hypotheses, and empirical strategies from a topic, phenomenon, or dataset. **Input:** `$ARGUMENTS` — a topic (e.g., "minimum wage effects on employment"), a phenomenon (e.g., "why do firms cluster geographically?"), or a dataset description (e.g., "panel of US counties with pollution and health outcomes, 2000-2020"). --- ## Steps 1. **Understand the input.** Read `$ARGUMENTS` and any referenced files. Check `master_supporting_docs/` for related papers. Check `.claude/rules/` for domain conventions. 2. **Generate 3-5 research questions** ordered from descriptive to causal: - **Descriptive:** What are the patterns? (e.g., "How has X evolved over time?") - **Correlational:** What factors are associated? (e.g., "Is X correlated with Y after controlling for Z?") - **Causal:** What is the effect? (e.g., "What is the causal effect of X on Y?") - **Mechanism:** Why does the effect exist? (e.g., "Through what channel does X affect Y?") - **Policy:** What are the implications? (e.g., "Would policy X improve outcome Y?") 3. **For each research question, develop:** - **Hypothesis:** A testable prediction with expected sign/magnitude - **Identification strategy:** How to establish causality (DiD, IV, RDD, synthetic control, etc.) - **Data requirements:** What data would be needed? Is it available? - **Key assumptions:** What must hold for the strategy to be valid? - **Potential pitfalls:** Common threats to identification - **Related literature:** 2-3 papers using similar approaches 4. **Rank the questions** by feasibility and contribution. 5. **Save the output** to `quality_reports/research_ideation_[sanitized_topic].md` --- ## Output Format ```markdown # Research Ideation: [Topic] **Date:** [YYYY-MM-DD] **Input:** [Original input] ## Overview [1-2 paragraphs situating the topic and why it matters] ## Research Questions ### RQ1: [Question] (Feasibility: High/Medium/Low) **Type:** Descriptive / Correlational / Causal / Mechanism / Policy **Hypothesis:** [Testable prediction] **Identification Strategy:** - **Method:** [e.g., Difference-in-Differences] - **Treatment:** [What varies and when] - **Control group:** [Comparison units] - **Key assumption:** [e.g., Parallel trends] **Data Requirements:** - [Dataset 1 — what it provides] - [Dataset 2 — what it provides] **Potential Pitfalls:** 1. [Threat 1 and possible mitigation] 2. [Threat 2 and possible mitigation] **Related Work:** [Author (Year)], [Author (Year)] --- [Repeat for RQ2-RQ5] ## Ranking | RQ | Feasibility | Contribution | Priority | |----|-------------|-------------|----------| | 1 | High | Medium | ... | | 2 | Medium | High | ... | ## Suggested Next Steps 1. [Most promising direction and immediate action] 2. [Data to obtain] 3. [Literature to review deeper] ``` --- ## Principles - **Be creative but grounded.** Push beyond obvious questions, but every suggestion must be empirically feasible. - **Think like a referee.** For each causal question, immediately identify the identification challenge. - **Consider data availability.** A brilliant question with no available data is not actionable. - **Suggest specific datasets** where possible (FRED, Census, PSID, administrative data, etc.).