Princeton University

CITP Lecture: Amanda Coston - Responsible Machine Learning through the Lens of Causal Inference

Machine learning algorithms are widely used for decision making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has ...
JSTOR Daily

Commentary: On Two Approaches to Weighting in Causal Inference

Setodji CM, McCaffrey DF, Burgette LF, Almirall D, Griffin BA. The right tool for the job: Choosing between covariate balancing and generalized boosted model propensity scores. Epidemiology. 2017.
Harvard Business School

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach ...
San Diego Reader

Rethinking Machine Learning: Stuart Frost Makes the Case for Causal AI in Manufacturing

The manufacturing landscape is evolving rapidly, with intelligent systems increasingly promising to boost efficiency, quality, and overall competitiveness. Traditional machine learning (ML) has ...
mccormick.northwestern.edu

MLDS 490: Bayesian Methods for Inference and Decision Making

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...