- Subhashini Venugopalan
- Arunachalam Narayanaswamy
- Samuel Yang
- Anton Geraschenko
- Scott Lipnick
- Nina Makhortova
- James Hawrot
- Christine Marques
- Joao Pereira
- Michael Brenner
- Lee Rubin
- Brian Wainger,
- Marc Berndl
Abstract
Confounding variables are a well known source of nuisance in biomedical studies. They present an even greater challenge when we combine them with black-box machine learning techniques that operate on raw data. This work presents two case studies. In one, we discovered biases arising from systematic errors in the data generation process. In the other, we found a spurious source of signal unrelated to the prediction task at hand. In both cases, our prediction models performed well but under careful examination hidden confounders and biases were revealed. These are cautionary tales on the limits of using machine learning techniques on raw data from scientific experiments.
Research Areas
Learn more about how we do research
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work