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 illuminated numerous examples where these algorithms proved unreliable or inequitable. In this talk I show how causal inference enables us to more reliably evaluate such algorithms’ performance and equity...