SERtalks Archives

April 29, 2019

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

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

Surviving or collapsing with causal inference

Air pollution and neurodevelopment: Translating from biology to epidemiology to policy

An Introduction to Quantitative Bias Analysis

Machine Learning and Causality: Considerations for Prediction and Effect Estimation

Inference After Machine Learning for Causal Effects

Demise of the rarity assumption in epidemiologic studies

Mediation: Contemporary Epidemiologic Tools to Evaluate Direct and Indirect Effects