brycewang-stanford Research Ideation
Generate structured research questions, testable hypotheses, and empirical strategies from a topic, phenomenon, or dataset.
Input: $ARGUMENTS — a topic, phenomenon, dataset description, or extensions to brainstorm extensions of the current paper.
Project-Specific Context
Available Data
- EDM data (2021-2024+): Event-level sewage spill records for all overflow sites in England
- Land Registry: Universe of property transactions (prices, dates, postcodes)
- Zoopla: Rental listings with prices and characteristics
- Met Office: Daily rainfall at LSOA level
- River networks: PostGIS topology of English waterways
- Annual returns: Water company infrastructure data (sewer capacity, population served)
- Geographic boundaries: LSOAs, MSOAs, constituencies, water company areas
Existing Identification Strategies
Hedonic, repeat sales, long difference, DiD with media coverage, upstream/downstream, dry spills, hydraulic capacity IV
Related Literature Areas
Environmental disamenity capitalisation, water quality and property values, UK water industry regulation, infrastructure and house prices, information and price discovery
Workflow
Step 1: Understand the Input
Read $ARGUMENTS and any referenced files.
- If
extensions: read current manuscript sections and analysis scripts to understand what's been done - If a topic: ground it in the available data and methods
Step 2: Generate 3-5 Research Questions
Order from descriptive to causal:
- Descriptive: What are the patterns? (e.g. "How do spill frequencies vary across water companies?")
- Correlational: What factors are associated? (e.g. "Is sewer age correlated with spill frequency after controlling for rainfall?")
- Causal: What is the effect? (e.g. "What is the causal effect of media coverage of sewage spills on house prices?")
- Mechanism: Why does the effect exist? (e.g. "Do dry spills affect prices through stigma or physical damage?")
- Policy: What are the implications? (e.g. "Would mandated real-time spill disclosure affect property markets?")
Step 3: Develop Each Question
For each:
- Hypothesis: Testable prediction with expected sign/magnitude
- Identification strategy: How to establish causality
- Data requirements: What data is needed — is it available in the project?
- Key assumptions: What must hold
- Potential pitfalls: Threats to identification
- Related literature: 2-3 papers using similar approaches
Step 4: Rank by Feasibility and Contribution
| RQ | Feasibility | Contribution | Priority |
|---|---|---|---|
| 1 | High/Med/Low | High/Med/Low | ... |
Step 5: Save Output
# Research Ideation: [Topic]
**Date:** YYYY-MM-DD
## Overview
[1-2 paragraphs situating the topic]
## Research Questions
### RQ1: [Question] (Feasibility: High/Medium/Low)
**Type:** Descriptive / Correlational / Causal / Mechanism / Policy
**Hypothesis:** [Prediction]
**Identification:** [Method + key assumption]
**Data:** [What's needed and what's available]
**Pitfalls:** [Top 2 threats]
**Related work:** [2-3 papers]
## Ranking
[Table]
## Suggested Next Steps
1. [Most promising direction]
2. [Data to obtain or analysis to run]
3. [Literature to review]
Save to output/log/research_ideation_[topic].md.
Principles
- Be creative but grounded. Every suggestion must be empirically feasible with available or obtainable data.
- Think like a referee. For each causal question, immediately identify the identification challenge.
- Leverage existing infrastructure. The project already has spatial matching, spill aggregation, and river network tools — build on them.
- Consider data availability. Flag when data is already available vs needs to be obtained.
- Extensions are valuable. For
extensions, focus on questions that use the same data infrastructure but ask different questions.