Title: Counterfactual Risk Assessments for Child Welfare Screening
Speaker: Amanda Coston
Risk assessments and other algorithmic decision-making systems are increasingly used in high-stakes applications such as criminal justice, consumer lending, and child welfare screening decisions. Many risk assessment models are trained on observational data where historical interventions may have affected the observed outcomes. Our research investigates how these observational risk models may be biased in the case of child welfare screening decisions, and we propose counterfactual risk assessments that account for the intervention affects.
Amanda is a joint PhD student in Machine Learning and Public Policy at Carnegie Mellon University. She is broadly interested in how machine learning can solve problems of societal interest, and her research areas include algorithmic fairness, causal inference, and machine learning for healthcare.