Misclassification
Likely biased but possibly useful: the implications of conditioning on future events for interpretation of effects in perinatal epidemiology
Session Chairs: Mollie Wood, Dominique Heinke
Epidemiologic methods for applied research: Answering important public health questions that are not causal
Session Chairs: Cathering Lesko, Matthew Fox
Assessment of recording bias in pregnancy studies using health care databases: An application to neurologic conditions.
Sarah McDonald, IQVIA LINK TO ARTICLE
Tyroler Student Prize Paper Award Winner
Brittany Blouin “Using Bayesian methods to adjust for exposure misclassification in a mediation analysis with multiple mediators: The role of hemoglobin levels and malnutrition in the association between Ascaris infection and IQ scores”
Why Causation Matters: Stanford’s Second Colloquium on Machine Learning and Causal Inference
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