About

Hi, I’m Caio Velasco 👋

I’m originally from Brazil. I studied Mechanical Engineering at the Federal University of Rio de Janeiro (UFRJ) and later completed a 2-year Master in Economics & Public Policy at the University of California Los Angeles (UCLA), supported by a full scholarship from the Lemann Foundation.

During my academic path, I was drawn increasingly toward quantitative reasoning - probability, econometrics, statistical inference - and to the deeper question behind them: Why do these methods work?

At some point, applying models was no longer enough. I wanted to understand more. This book is the result of that shift.

Why This Book Exists

Throughout my studies, I encountered powerful tools: regression, asymptotics, optimization, machine learning, causal inference.

But I realized that true clarity requires something deeper:

• Understanding convergence, not just using estimators.
  
• Understanding probability spaces, not just distributions.
  
• Understanding identifiability, not just running models.

This project is my attempt to rebuild the mathematical backbone behind data science and causal machine learning - slowly, rigorously, and honestly.

What Drives Me

• Rigor. I care about knowing where assumptions live.
  
• Structure. I like seeing how ideas connect across layers.
  
• Clarity. I prefer fewer tools, understood deeply.
  
• Intellectual honesty. This book reflects what I have actually studied to get here.
  
• Sharing. I am motivated by learning in public and helping demystify ideas that often appear more complex than they need to be.

I believe that prediction without foundations is fragile, and causal claims without structure are dangerous.

So this journey begins at the base:

• logic, linear algebra, analysis, measure, probability and builds upward toward causal reasoning.

If this book does anything well, I hope it shows that mathematical depth is not about abstraction for its own sake, but about understanding the layer you are standing on.