Dynamic Treatment Regimes

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

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

Precision Medicine and Causal Inference – Beyond the Average Treatment Effect