Teaching and Learning

Semester-Long Courses

  • Econometrics I at UC Berkeley (EC140), 2022. Teaching materials
  • Introduction to Environmental Economics & Policy at UC Berkeley (EEP1/EC3), 2020
  • Econometrics I at Universidad de Chile, 2007, 2008, 2010

Below, in Self-Directed Courses, are short courses I built to learn or refresh tools and methods for industry roles that fit my background.

Teaching Statement

Self-Directed Courses

I am also exploring private-sector positions in the Bay Area. To support this transition, I have been building a series of short, self-directed courses to refresh and develop skills relevant to the roles I’m targeting, such as policy researcher, applied policy economist, and data scientist positions at technology platforms.

The courses below were built using Claude Code. Each course is a set of xaringan slide decks with worked examples, R/Python/SQL exercises, and interview-style practice questions, all threaded through industry-related applications.

  • ML Refresher: Discrimination and Fairness. Bias-variance tradeoff, linear models, model evaluation, fairness frameworks, and the impossibility theorem. Every module pairs an ML concept with a discrimination scenario.
  • Discrimination in Labor Economics Refresher. Becker, Phelps, audit studies (Ge et al. 2016), Oaxaca-Blinder decomposition (Cook et al. 2021), algorithmic audits, and the practitioner playbook.
  • Intro to Experiments in Industry. Potential outcomes and the experimental ideal, SUTVA and interference, cluster/switchback designs, regression adjustment and CUPED, ITT vs LATE, and external validity.
  • Causal Inference Beyond A/B. Deep-dive course on causal inference for platform settings: modern DiD, synthetic control, causal forests, policy learning, and matrix completion.
  • Intro to SQL. SELECT through window functions, drilled on a synthetic ride-sharing schema. Five modules of interview-style questions designed to be answerable from a cold start in under two minutes each. One pager.
  • Python for an R User. The pandas + statsmodels stack, framed as a dplyr-to-pandas translation. Covers data wrangling, joins, OLS, A/B test inference, and difference-in-differences, with end-to-end interview scenarios.