Jim Barrett and Pian Chen | August 4, 2026
A model the court can understand, probe, and trust is worth more than an elegant black box, because in the end the models that decide cases are the ones that survive scrutiny.
Rui Huang and Pian Chen | June 24, 2026
This article is the second part of our data science crash courses — Causal Methods in the Courtroom and the C-Suite. The courses introduce fundamental methods that economists, statisticians, and data scientists use to make causal inference. We write for two groups: (1) lawyers who want to understand whether challenged conduct caused harm and how to quantify damages and (2) business leaders who make product, pricing, and marketing decisions.
Rui Huang and Pian Chen | June 12, 2026
This article is the first part of our data science crash courses — Causal Methods in the Courtroom and the C-Suite. The courses introduce fundamental methods that economists, statisticians, and data scientists use to make causal inference. We write for two groups: (1) lawyers who want to understand whether challenged conduct caused harm and how to quantify damages and (2) business leaders who make product, pricing, and marketing decisions. This article focuses on experimental methods for causal inference. Later installments in this series will cover the statistical, econometric, and machine-learning methods that have been tested in the courtroom or have been used by business leaders.
