Dr. Juan Camilo Orduz

On many occasions, I read about Multilevel Regression and Poststratification (MRP) in the context of survey analysis (see for example Multilevel Regression and Post-stratification in Bambi).

In this notebook we implement some of the ideas of the previous blog post “Introduction to Causal Inference with PPLs” using PathMC, a package for structural causal models with Bayesian estimation and interventional simulation via a concise Domain-Specific

This example is part of the NumPyro Forecast documentation; see here.

In this notebook, we work out an example of how to interpret and communicate statistical models.

In the blog post “Notes on Exponential Smoothing with NumPyro” we implemented the damped Holt-Winters model in the component form of Hyndman & Athanasopoulos, FPP3, §8.3.

In this notebook we provide an introduction to the frugal parameterization for Bayesian causal inference, developed in the paper “Parameterizing and simulating from causal models”, (Evans & Didelez, 2024).

Mediation analysis goes beyond asking “does the treatment work?”

In many forecasting applications, probabilistic methods have become a standard tool.

This notebook implements a simulation-based Bayesian power analysis for conversion-rate A/B tests and compares it against the classical frequentist two-proportion z-test.

In many years of professional data science, I have seen many senior decision makers choose models based on in-sample \(R^2\) — the more the better.

In this notebook we reproduce the fantastic material from the video Statistical Rethinking 2026 Lecture B04 - Group-level confounding and intro to social networks by Richard McElreath.

In this notebook I explore the Bayesian Decision Theory Workflow described in the amazing blog post A Bayesian Decision Theory Workflow by Daniel Saunders.

In this third notebook, I extend the hierarchical forecasting model from Part II by adding a neural network component to the state transition function.

Based on my experience, whenever someone asks for a prediction (or forecasting) model, they actually do not need a prediction model per se.

In this notebook, we demonstrate how to estimate Conditional Average Treatment Effects (CATE) using a Causal Effect Variational Autoencoder (CEVAE) by implementing an example from scratch in NumPyro.

This notebook demonstrates how to estimate the Average Treatment Effect (ATE) using variational inference in the presence of unobserved confounders.

Estimating causal effects from clustered or grouped data requires careful attention to the hierarchical structure of observations.

Causal inference asks a deceptively simple question: “What would have happened if things were different?”

In this notebook, we present how to implement and fit Bayesian Vector Autoregressive (VAR) models using NumPyro.

In this notebook we provide a brief introduction to Stochastic Variational Inference (SVI) with NumPyro.

In this notebook, we extend the revenue-retention model introduced in the sequence of blog posts “Cohort Revenue & Retention Analysis: A Bayesian Approach” and “Cohort Revenue Retention Analysis with Flax and NumPyro” (plus the associated pre-print “Cohort

In this notebook, we present a simple example to illustrate how to vectorize the ROC curve computation over a Bayesian model.

Recently, I have been thinking a lot about data-driven decision-making, particularly in the context of experimentation.

In this notebook we generate a simulation to reproduce the results of the great blog post “The Bet Test: Spotting Problems in Bayesian A/B Test Analysis”, where Tyler Buffington discussed about some caveats of using Bayesian AB testing when not thinking a…

In this notebook, we want to explore some ideas on hierarchical Hilbert Space Gaussian Processes following the fantastic exposition of the PyMC example notebook: “Gaussian Processes: HSGP Advanced Usage” by Bill Engels, Alexandre Andorra and Maxim Kochuro…

In many demand forecasting problems, we face the challenge of predicting the demand for a product or service with very sparse data.

Motivated by the great blog post by Understanding CUPED by Matteo Courthoud, we explore a Bayesian approach to CUPED to understand its sensitivity with respect to the covariance parameter \(\theta\) of the pre-post mean model.

We present an example of a dynamic forecasting time-series model that incorporates a prior calibration process to estimate the temperature effect on electricity demand.

We work out a classical electricity demand forecasting model form the case study Structural Time Series Modeling Case Studies: Atmospheric CO2 and Electricity Demand from the TensorFlow Probability documentation.

In this second notebook, we continue working on the NumPyro implementation of the hierarchical forecasting models presented in Pyro’s forecasting documentation: Forecasting III: hierarchical models.

In this notebook we provide a NumPyro implementation of the first model presented in the Pyro forecasting documentation: Forecasting III: hierarchical models.

In this notebooks we port the Pyro forecasting example Forecasting I: univariate, heavy tailed to NumPyro.

In this notebook we use a retail publicly available dataset to fit and compare various pricing elasticity models.

In this notebook we continue our simulation study for elasticities, see: Multilevel Elasticities for a Single SKU - Part I Multilevel Elasticities for a Single SKU - Part II for an introduction to the problem and some models.

In this blog post, we experiment with a hierarchical exponential smoothing forecasting model, extending the ideas from the univariate case presented in the blog post “Notes on Exponential Smoothing with NumPyro”.

In this notebook we will explore the use of censored likelihoods for demand forecasting.

In this notebook, we explore the (conceptual) ideas and (practical) implementation details of the Hilbert Space approximation for Gaussian processes introduced in the article “Hilbert space methods for reduced-rank Gaussian process regression” by Arno Sol…

In this notebook, we explore how we can use Bayesian modeling to estimate the parameters of a censored data set.

After going through the fundamentals of the TSB Method for Intermittent Time Series Forecasting in NumPyro in the previous notebook, we explore a variation of it that might be useful for certain applications.

In this notebook we provide a NumPyro implementation of the TSB (Teunter, Syntetos and Babai) method for forecasting intermittent time series.

In this notebook, we will implement Croston’s method for intermittent demand forecasting using NumPyro.

This are some notes on how to implement an ARMA(1, 1) model using NumPyro for time series forecasting.

This notebook serves as personal notes on NumPyro’s implementation of the classic exponential smoothing forecasting method.

In this notebook, we present a complete simulation study of the media mix model (MMM) and experimental calibration method presented in the paper “Media Mix Model Calibration With Bayesian Priors”, by Zhang, et al., where the authors propose a convenient p…

In this notebook we present an alternative implementation of the cohort-revenue-retention model presented in the blog post Cohort Revenue & Retention Analysis: A Bayesian Approach where we show how to replace the BART retention component with a general ne…

In this notebook I want to experiment with the numpyro/contrib/module.py module which allow us to integrate Flax models with NumPyro models.

In this notebook we want to reproduce a classical example of using Gaussian processes to model time series data: The birthdays data set.

In this notebook we present an example of how to use a combination of Bayesian hierarchical models and the non-parametric methods , namely bayesian additive trees (BART), to model the product life cycles.

In this notebook we describe how to use blackjax’s pathfinder implementation to do inference with a numpyro model.

I had the great opportunity to have a conversation with Aleksander Molak about causality, marketing and simulations as well as my career from academia to industry.