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johnbarraza/README.md

John Barraza

Economics · Data Science · Public Policy · Applied AI

Economics student at Universidad del Pacífico and Research Assistant at CIUP.
I use economic research, data and technology to study public policy problems
and build practical data-driven tools.

Personal Website · LinkedIn


About me

  • Economics student at Universidad del Pacífico (top quintile)
  • Research Assistant at CIUP
  • Interested in causal inference, education and public policy
  • Building applied AI and data products in Peru: SumaYa , FarmaciaIA, Aptito.

Selected projects

Project Description
HA Aiyagari 2 firms (formal and informal) in Peru Macroeconomic model about Heterogenous Agentes with 2 sector: formal and informal in Peru
Top Papers Creator Automated pipeline for producing academic economics papers using LLM-assisted reasoning, Python execution and human review.
DAGs and CausalML Notebooks and applications related to causal inference, directed acyclic graphs and machine learning.
Decision Trees Practical implementations and analysis of decision-tree models using Jupyter notebooks.

Tools

Python · R · Stata · SQL · MATLAB · LaTeX
Flutter · FastAPI · Supabase · GitHub Actions

Research interests

Causal inference · Impact evaluation · Education · Labor informality
Public policy · Economic modeling · Applied artificial intelligence

GitHub activity



John Barraza's contribution activity graph


Building at the intersection of economics, data and technology.

Pinned Loading

  1. aiyagari-2firms-peru aiyagari-2firms-peru Public

    MATLAB

  2. DAGs_CausalML DAGs_CausalML Public

    Jupyter Notebook

  3. Decision_Trees Decision_Trees Public

    Jupyter Notebook

  4. Top_papers_creator Top_papers_creator Public

    Automated production of academic economics papers — Claude handles reasoning, Python handles execution. 14 stages, 2 human checkpoints.

    Python

  5. UPBlackboardSync UPBlackboardSync Public

    Forked from sanjacob/BlackboardSync

    Download your Blackboard Learn UP content automatically

    Python