Machine Learning

April 29 2019

Why Causation Matters: Stanford’s Second Colloquium on Machine Learning and Causal Inference

Suzanne Bevan SERtalks, Video, Webinar Causal Inference, Confounding, Doubly Robust Estimators/TMLE, Dynamic Treatment Regimes, Flexible Modeling, Health Disparities, HIV/STI, Instrumental Variable, Interaction/Effect Measure Modification, Machine Learning, Mental Health, Misclassification, P-Values, Propensity Score, Quasi-Experimental, Race, Randomized Controlled Trial, Selection Bias, Social, Statistics (e.g. Hypothesis Testing), Synthetic Control

Jennifer Hill, Susan Athey, Maria Glymour, Jennifer Ahern, Daniel Ho, Guido Imbens, Kristian Lum, Nigam Shah, Maya Petersen Why Causation Matters – Welcome Why Causation Matters – Jennifer Hill Why Causation Matters – Heterogeneity-Estimation Why Causation Matters – Prediction-Fairness Why Causation Matters – Design

Machine Learning and Epidemiology

Challenges and Opportunities for Causal Inference in Molecular Epidemiology

Mendelian Randomization

Machine Learning

Leveraging mobile health applications for epidemiologic research

Current Approaches and Applications in Investigating Gene/Environment Interactions

Tracking the Burden of Substance Abuse