Dr. Juan Orduz
Mathematician (Ph.D., Humboldt Universität zu Berlin) and data scientist. I am interested in interdisciplinary applications of mathematical methods, particularly time series analysis, Bayesian methods, and causal inference. Active open source developer (PyMC, PyMC-Marketing, and NumPyro, among others). For more info, please visit my personal website https://juanitorduz.github.io
@juanitorduz.bsky.social
Session
Causal inference asks the hardest question in data science: "What would have happened if things were different?" While traditional methods often rely on rigid rules, statistical tests or "black box" adjustments, Probabilistic Programming Languages (PPLs) like PyMC and NumPyro offer a transparent, flexible, and powerful lens to view these problems.
In this talk, we move beyond the standard "correlation is not causation" disclaimer. We will build a unified workflow that starts with robust A/B testing, moves to bias adjustment in observational data using multilevel models, and culminates with advanced Deep Causal Latent Variable Models (CEVAE).