Machine Learning

July 5 2022

Machine Learning and Epidemiology

Suzanne Bevan 2022, Annual Meeting Recording Machine Learning, Methods/Statistics

May 23 2022

Challenges and Opportunities for Causal Inference in Molecular Epidemiology

Suzanne Bevan Methodological Playlists, SERplaylists Machine Learning

Jonathan Huang and Brian Whitcomb explore the complexities of causal inference in molecular epidemiology. This playlist highlights key challenges, such as confounding and measurement error, and discusses opportunities for leveraging advanced methods to improve study validity and interpretation.

February 1 2022

Mendelian Randomization

Suzanne Bevan Methodological Playlists, SERplaylists Machine Learning

Jeremy Labrecque explains Mendelian randomization as a method for strengthening causal inference in epidemiology. This playlist explores how genetic variants serve as instrumental variables to reduce confounding and improve the validity of observational research.

January 18 2022

Machine Learning

Suzanne Bevan Applied Playlists, Methodological Playlists, SERplaylists Machine Learning

Ashley Naimi introduces machine learning applications in epidemiology, explaining how these advanced methods can uncover patterns, improve predictions, and enhance public health research. This playlist explores opportunities and challenges in integrating machine learning into epidemiologic studies.

March 11 2020

Leveraging mobile health applications for epidemiologic research

Suzanne Bevan Society Collaborations, Video, Webinar Big Data, Machine Learning, Perinatal, Reproductive, Survey Sampling, Women's Health

SER-SPER Collaboration Moderator: Sonia Grandi, NIH Panel Presenters: Carlotta Favaro, Jenna Nobles, Nitika Pant Pai, Quynh Pham, Shruthi Mahalingaiah

September 4 2019

Current Approaches and Applications in Investigating Gene/Environment Interactions

Suzanne Bevan Society Collaborations, Video, Webinar Cancer, Environment, Genetics, Genomic Methods, Interaction/Effect Measure Modification, Machine Learning, Mathematical Modeling, Methods/Statistics

June 19 2019

Tracking the Burden of Substance Abuse

Suzanne Bevan 2019, Annual Meeting Recording, Video Drug Overdose, Global Health, Machine Learning, Substance Use

June 19 2019

Methods That Acknowledge, Address, and Even Embrace Data Complexity

Suzanne Bevan 2019, Annual Meeting Recording, Video Causal Inference, Competing Risks, Confounding, G-Methods, HIV/STI, Instrumental Variable, Inverse Probability Weight, Machine Learning, Methods/Statistics, Pharmacoepidemiology, Randomized Controlled Trial, Simulation, Social

May 8 2019

SERforum Live – Ana Diez Roux

Suzanne Bevan SERexperts, SERforum, Video, Webinar Age-Period-Cohort, Causal Inference, Early Stage Investigator, Ethics, Health Services/Policy, Machine Learning, Mentoring, Methods/Statistics, P-Values, Philosophy of Science/Epidemiology, Professional Development, Regression Discontinuity, Science Communication, Social, Statistics (e.g. Hypothesis Testing)

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