Juan C. Yamin

How much should a small ramp-up change the experiment that follows?

When and How to Pilot — solo-authored · arXiv:2607.16982

Digital experiments often run in two waves: a small ramp-up precedes the full experiment, and the ramp-up data can inform the main-wave traffic split. Splitting evenly ignores what the ramp-up learned about which arm is noisier; plugging noisy variance estimates into the Neyman formula can overreact badly — with a small ramp-up it can assign a live arm almost no traffic. My rule is a closed-form traffic split that moves from an even split toward the optimal allocation only as far as the ramp-up's evidence has earned, and reports a finite-sample certificate bounding what the move can cost.

• Covers A/B/n tests with shared control, stratified splits across user segments or markets, and ramp-ups that observe a fast proxy while the primary metric arrives with delay.
• Answers the sizing question too: with an experimentation budget of B observations, the pilot should be roughly B^2/3; for a 100,000-observation budget, that is about 2,000 pilot observations.
• In calibrations to Upworthy headline tests and Criteo's 13.9-million-user ad-experiment archive, the rule avoids feasible Neyman's severe small-ramp-up losses — and in the Criteo calibration captures about 90% of the attainable precision gain by 4,000-user ramp-ups.

Targeting scarce resources when information is imperfect

Poverty Targeting with Imperfect Information — solo-authored · arXiv:2506.18188

The paper studies a broader prioritization problem: how to allocate a fixed budget when decisions must be based on estimates rather than direct observations. In the antipoverty-targeting application, I show that the standard plug-in rule, which treats estimates as truth, is systematically improvable: another feasible rule can do at least as well in every case and better in some cases. I then build a nonparametric empirical Bayes rule that first learns how much to trust each estimate — using the same data, the same prediction model, and the same budget.

• In simulations on household surveys from nine African countries, the rule reaches nearly 80% more of the target population than plug-in ranking (45.6 vs. 25.5 per 1,000) and outperforms plug-in OLS and machine-learning benchmarks.

Applied causal research from question to data to interpretation

Political economy, development, administrative data

Alongside my methods work, I have carried traditional applied-economics projects from institutional question to data construction, identification, robustness, and policy interpretation. In coauthored research conditionally accepted at the American Political Science Review, I study whether partisan alignment across levels of government facilitates corruption in Colombia: the project combines legal and institutional analysis, administrative education records, close-election regression discontinuity, validation and robustness checks, and evidence on ghost enrollment, discretionary hiring, outsourcing, and electoral fraud risk.

Earlier at Universidad de los Andes, I coordinated multi-year data collection, digitization, replication, and empirical analysis across development and political-economy projects. That work included contributions to projects involving Daron Acemoglu, Simon Johnson, and James A. Robinson, the 2024 Economics Nobel laureates — including a Review of Economic Studies paper with Acemoglu and Johnson on population growth and conflict, plus projects on radio education, policy implementation, Facebook and protests, and household shocks.