About

Using AI to accelerate rigorous economic research—and showing you how to do the same.

Why "Too Early To Say"?

In 1971, Zhou Enlai was asked about the impact of the French Revolution. He replied: "It is too early to say." The quote became shorthand for patience in analysis—the idea that rushing to conclusions undermines rigor.

For decades, researchers accepted this tradeoff. Rigorous research took years. By the time findings reached policymakers, the policy window had closed. Rigor required time, and time killed relevance.

Generative AI changes the equation. AI removes friction and, with reasonable application, can do that without lowering standards. Data collection that took months now takes days. Literature reviews that required weeks can be completed in hours. Documentation happens in real time, not as an afterthought. The careful work still happens. The difference is speed.

When AI removes the bottleneck, the relative timeline around "too early to say" fundamentally shifts. This site proves when you can say it sooner without saying it carelessly—and teaches you how to do the same.

How It Works

AI accelerates every stage: data collection and cross-referencing, validation and quality control, real-time methodology documentation, and literature synthesis. The result is research that meets peer-review standards but arrives while policymakers can still act on it.

Every analysis includes full methodology, data sources, and reproducible code. Every workflow is documented for replication.

What We Publish

Two types of content, designed to work together:

Applied Research — Original analysis across applied microeconomics: labor, health, education, environment, public finance, social services. Each article includes full methodology and replication materials.
AI Research Methods — Detailed walkthroughs of how we use AI to accelerate rigorous work. Data collection pipelines, validation workflows, literature synthesis, documentation practices. Everything you need to build your own AI-augmented research process.

The research demonstrates the approach. The methods articles teach it.

About the Author

Victoria Cholette, PhD

Victoria holds a PhD in Health Care Management and Economics from The Wharton School, University of Pennsylvania. With experience across academic research and public health practice, her independent research examines how economic barriers affect working families' access to essential resources across food security, transit accessibility, education finance, and health outcomes. She specializes in extracting actionable insights from large-scale administrative and public datasets through a combination of advanced causal inference methods (difference-in-differences, synthetic control, causal forests), generative AI, and machine learning. Her approach integrates econometric analysis with AI-assisted workflows to address complex policy questions, grounded in transparent, reproducible documentation. Her work is conducted independently through open-source platforms and peer-reviewed publications.

The analyses here represent personal research interests and do not reflect any organization or institution.

Contact

For research inquiries, corrections, or collaboration: [email protected]