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

# 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.).