With a Bachelor's degree in Economics from UNALM, Peru, and a Master's degree from UdeSA, Argentina, my expertise lies at the confluence of causal inference and machine learning, with a focus on the political economy and environmental conservation. I have developed key tutorials for DoubleML and contributed to 'Applied Causal Inference Powered by ML and AI,' demonstrating my commitment to integrating econometric rigor with practical data science applications.

I am dedicated to devising methodologies for gleaning actionable insights from unstructured data sets to inform policymaking, leveraging advanced computing resources and cutting-edge AI technologies such as Large Language Models (LLMs), Natural Language Processing (NLP), Optical Character Recognition (OCR), and High-Performance Computing (HPC). My proficiency also encompasses econometric software tools designed for the analysis of extensive data sets exceeding 1TB.

My research evaluates the impact of various policies on forest conservation and agricultural areas. Initially, I investigate the influence of the Land Record Modernization Programme (regarding land titles and registration) on forested regions. Furthermore, I am examining how overlapping jurisdictions impact forest conservation efforts, employing regression discontinuity, diff-and-diff estimations and Synthetic Control Method.