Unobserved Confounders: How Admitting What We Don’t Know Can Unlock Data’s Full Potential
Unobserved confounders have long been considered a limiting factor in the accuracy of statistical models, but machine learning’s ability to approximate underlying patterns in data opens up new possibilities. Acknowledging and repositioning these unknown, non-linear relationships as fundamental attributes of any dataset presents an opportunity to advance statistical theory and its applications in machine learning. […]
