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Understanding Leniency Designs: Why Your Examiner Matters (And How to Estimate It Right)

causal inference

instrumental variables

methodology

A breakdown of Goldsmith-Pinkham, Hull, and Kolesár (2025), showing why standard 2SLS fails in judge IV designs with many instruments and how UJIVE solves the problem. Interactive simulations reveal the bias.

Nov 20, 2025

Jacob Jameson

Beyond DiD: Autoregressive Models for Policy Evaluation When Parallel Trends Fail

causal inference

policy evaluation

methodology

This post demonstrates how autoregressive models can overcome the limitations of difference-in-differences analysis when evaluating health policies with non-parallel pre-treatment trends, providing researchers with practical tools to improve causal inference in observational studies.

Apr 29, 2025

Jacob Jameson

A Gentle Introduction to Bayesian Linear Regression in R

bayesian

regression

This guide walks you through Bayesian linear regression in R and Stan, explaining how priors, likelihoods, and posteriors work in an intuitive and hands-on way.

Feb 9, 2025

Jacob Jameson

DIY Decision Trees

decison trees

machine learning

Embarking on a DIY Journey with Machine Learning Algorithms.

Mar 26, 2024

Jacob Jameson

Does it Spark Joy? Using PCA for Dimensionality Reduction in R

PCA

Principal Component Analysis (PCA) is a powerful tool for dimensionality reduction. In this post, we provide an overview of PCA, explaining its purpose and how it works. We then walk through a hands-on application of PCA using R, demonstrating the step-by-step process of PCA and its application in predictive modeling.

Feb 27, 2024

Jacob Jameson