Organizing Committee
  • Joseph Hogan
    Brown University
Abstract

Causal and mathematical models are widely used for decision making and policy evaluation at both the micro and macro levels. For example, causal models using large datasets are used to evaluate treatment efficacy in HIV; mathematical models are used to simulate the effects of prevention or policy measures to improve health outcomes or reduce the spread of infectious diseases. Entities such as the World Health Organization and UNAIDS rely on these models to set wide-ranging and high-impact policy related to treatment and prevention of infectious disease.

Causal models tend to rely on large-scale cohort data, while mathematical models in many ways represent evidence synthesis. Important methodologic issues in the development, application, and interpretation of these models include the role of untestable assumptions, transportability of findings to specific populations of interest, model calibration and validation, and uncertainty quantification. The datasets used to develop these models are complex in nature.

This workshop will bring together leading researchers in the areas of modeling, machine learning and causal inference to delve more deeply into foundational and methodologic issues and their implications, illustrate the use of these models in real-world settings, and draw connections between the two approaches.

Key questions to be addressed and discussed include:

  • What is the role of an underlying causal model in decision making?
  • How do we quantify uncertainty from multiple sources, such as model selection, untestable assumptions, prediction uncertainty?
  • What is the role of predictive models in causal inference?
  • What are the connections between statistical models and mathematical agent-based models for drawing causal inferences?
  • What is the role of machine learning in inferring causal relationships and decision making?
  • How should methods be adapted to specialized settings (e.g., social networks)?
This workshop is part of the Brown Data Science Initiative project, funded by the National Science Foundation's TRIPODS program.

Image for "Models and Machine Learning for Causal Inference and Decision Making in Health Research"
This workshop is part of the Brown Data Science Initiative project, funded by the National Science Foundation's TRIPODS program.

Confirmed Speakers & Participants

Talks will be presented virtually or in-person as indicated in the schedule below.

  • Speaker
  • Poster Presenter
  • Attendee
  • Virtual Attendee
  • Giorgos Bakoyannis
    Indiana University
  • Angela Bengtson
    Brown University
  • David Benkeser
    Emory University
  • Ashley Buchanan
    University of Rhode Island
  • Changqing Cheng
    Binghamton University
  • Seung-Ah Choe
    Brown University
  • Nam-Kyong Choi
    Ewha woman's university
  • Stavroula Chrysanthopoulou
    Brown University
  • Leah Comment
    Harvard T.H. Chan School of Public Health
  • Sarah Conner
    Boston University
  • Terry-Ann Craigie
    Connecticut College
  • Lorin Crawford
    Brown University
  • Issa Dahabreh
    Brown University
  • Michael Daniels
    University of Florida
  • Valery Danilack
    Brown University / Women & Infants Hospital
  • Dennis Dean
    Seven Bridges
  • Ivan Diaz
    Weill Cornell Medicine
  • Alexander Fengler
    Brown University
  • Aaron Fisher
    Harvard
  • Colin Fogarty
    MIT
  • Shaun Forbes
    Brown University
  • Elizabeth Fussell
    Brown University
  • Omar Galarraga
    Brown University School of Public Health
  • Jason Gantenberg
    Brown University
  • Ilana Gareen
    Brown University
  • Constantine Gatsonis
    Brown University School of Public Health
  • Roee Gutman
    Brown University
  • Christopher Halladay
    Providence VAMC
  • Babak Hemmatian Borujeni
    Brown University
  • Joseph Hogan
    Brown University
  • Nathan Kallus
    Cornell University
  • Luke Keele
    University of Pennsylvania
  • Chanmin Kim
    Boston University
  • Taehee Lee
    Brown University
  • Benjamin Linas
    Boston University
  • Tao Liu
    Brown University
  • Alex Luedtke
    University of Washington
  • Brandon Marshall
    Brown University School of Public Health
  • Kun Meng
    Brown University
  • Eleanor Murray
    Harvard University SPH
  • Sharon-Lise Normand
    Harvard Medical School
  • Sangshin Park
    Brown University
  • Jonah Popp
    Brown University
  • Kexin Qu
    Brown University
  • Jason Roy
    Rutgers University
  • Christopher Schmid
    Brown University
  • Kenichi Shimizu
    Brown
  • Ilya Shpitser
    Johns Hopkins
  • Gabriella Silva
    Brown University
  • Dale Steele
    Alpert Medicine School
  • Jon Steingrimsson
    Brown University
  • Alisa Stephens-Shields
    University of Pennsylvania
  • shengzhi Sun
    Brown University
  • Adith Swaminathan
    Microsoft Research
  • Kemul Semir Tatlidil
    Brown University
  • Alexander Volfovsky
    Duke University
  • Isabelle Weir
    Boston University School of Public Health
  • Laura White
    Boston University
  • Marta Wilson-Barthes
    Brown University
  • Xiaotian Wu
    Brown University
  • Isabel Zhang
    Brown University
  • Yi Zhao
    Johns Hopkins University

Workshop Schedule

Monday, January 14, 2019
TimeEventLocationMaterials
8:30 - 8:55am ESTRegistration - ICERM 121 South Main Street, Providence RI 0290311th Floor Collaborative Space 
8:55 - 9:00am ESTWelcome - ICERM Director11th Floor Lecture Hall 
9:00 - 9:30am ESTWorkshop Overview - Joe Hogan, Brown University11th Floor Lecture Hall 
9:30 - 10:00am EST(Fast) Rates for Estimating Optimal Individualized Treatment Rules - Alex Luedtke, University of Washington11th Floor Lecture Hall
10:15 - 10:45am ESTLearning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes - Ivan Diaz, Weill Cornell Medicine11th Floor Lecture Hall
11:00 - 11:15am ESTCoffee/Tea Break11th Floor Collaborative Space 
11:15 - 11:45am ESTMachine learning methods for causal inference from complex observational data - Alexander Volfovsky, Duke University11th Floor Lecture Hall
12:00 - 12:30pm ESTComparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications - Luke Keele, University of Pennsylvania11th Floor Lecture Hall
12:30 - 2:00pm ESTBreak for Lunch / Free Time  
2:00 - 2:30pm ESTCausal Inference- Why Bayes - Michael Daniels, University of Florida11th Floor Lecture Hall
2:45 - 3:15pm ESTToward Evaluation of Dissemination of HIV Prevention Interventions Among Networks of People who Inject Drugs - Ashley Buchanan, University of Rhode Island11th Floor Lecture Hall
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:30pm ESTBayesian Methods for Multiple Mediators- Relating Principal Stratification and Causal Mediation - Chanmin Kim, Boston University School of Public Health11th Floor Lecture Hall
4:30 - 4:45pm ESTDiscussion 11th Floor Lecture Hall 
4:45 - 6:15pm ESTWelcome Reception11th Floor Collaborative Space 
Tuesday, January 15, 2019
TimeEventLocationMaterials
9:00 - 9:30am ESTStatistical approaches to understanding Tuberculosis transmission dynamics - Laura White, Boston University11th Floor Lecture Hall
9:45 - 10:15am ESTAgent-Based Models for Evaluating HIV Prevention Modalities in Sexual and Injecting Networks - Brandon Marshall, Brown University11th Floor Lecture Hall
10:30 - 10:45am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:45 - 11:15am ESTMicrosimulation Models in Medical Decision Making – Calibration and Predictive Accuracy - Stavroula Chrysanthopoulou, Brown University11th Floor Lecture Hall
11:30 - 12:00pm ESTChallenges of Mediation Analysis in Neuroimaging Studies - Yi Zhao, Johns Hopkins University11th Floor Lecture Hall
12:00 - 12:15pm ESTDiscussion 11th Floor Lecture Hall 
12:15 - 2:00pm ESTBreak for Lunch / Free Time  
2:00 - 2:30pm ESTUsing systems models for causal inference- a data-assumption trade-off - Eleanor Murray, Harvard TH Chan School of Public Health11th Floor Lecture Hall
2:45 - 3:15pm ESTBayesian nonparametrics and g-computation for causal inference in observational studies - Jason Roy, Rutgers School of Public Health11th Floor Lecture Hall
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:30pm ESTMultiple Imputation Procedure for Record Linkage and Causal Inference to Estimate the Effects of Home-delivered Meals - Roee Gutman, Brown University11th Floor Lecture Hall
4:30 - 4:45pm ESTDiscussion 11th Floor Lecture Hall 
Wednesday, January 16, 2019
TimeEventLocationMaterials
9:00 - 9:30am ESTNonparametric super-efficient estimators of treatment effects - David Benkeser, Emory University11th Floor Lecture Hall
9:45 - 10:15am ESTData Efficient Causal Effect Estimation - Adith Swaminathan, Microsoft Research11th Floor Lecture Hall
10:30 - 11:00am ESTCoffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:30am ESTSubgroup Identification using Covariate Adjusted Interaction Trees - Jon Steingrimsson, Brown University11th Floor Lecture Hall
11:45 - 12:00pm ESTDiscussion 11th Floor Lecture Hall 
12:00 - 12:15pm ESTGroup Photo11th Floor Lecture Hall 
12:15 - 2:00pm ESTBreak for Lunch / Free Time  
2:00 - 2:30pm ESTEstimating Treatment Effects in Clinical Trials Under Unobserved Conditions - Alisa Stephens Shields, University of Pennsylvania11th Floor Lecture Hall
2:45 - 3:15pm ESTExtending causal inferences from randomized trials to a target population - Issa Dahabreh, Brown University11th Floor Lecture Hall
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:30pm ESTPersonalizing Causal Pathway Effects - Ilya Shpitser, Johns Hopkins University11th Floor Lecture Hall
4:30 - 4:45pm ESTDiscussion 11th Floor Lecture Hall 
Thursday, January 17, 2019
TimeEventLocationMaterials
9:00 - 9:30am ESTLearning to Personalize from Observational Data Under Unobserved Confounding - Nathan Kallus, Cornell University & Cornell Tech11th Floor Lecture Hall
9:45 - 10:15am ESTStudentized Sensitivity Analysis in Paired Observational Studies - Colin Fogarty, Massachusetts Institute of Technology11th Floor Lecture Hall
10:30 - 10:45am ESTCoffee/Tea Break11th Floor Collaborative Space 
10:45 - 11:15am ESTCausal Inference, Uncertainty, and Health Policy Decision Making - Sharon-Lise Normand, Harvard Medical School11th Floor Lecture Hall
11:30 - 12:00pm ESTUsing incomplete electronic health record data to inform decision making in HIV and AIDS - Giorgos Bakoyannis, Indiana University11th Floor Lecture Hall
12:00 - 12:30pm ESTDiscussion, Wrap up of scientific program 11th Floor Lecture Hall 
12:30 - 2:00pm ESTBreak for Lunch / Free Time  
2:00 - 3:30pm ESTCollaborative Discussions 11th Floor Lecture Hall 
3:30 - 4:00pm ESTCoffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45pm ESTCollaborative Discussions 11th Floor Lecture Hall 

Lecture Videos

Using incomplete electronic health record data to inform decision making in HIV and AIDS

Giorgos Bakoyannis
Indiana University
January 17, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Causal Inference, Uncertainty, and Health Policy Decision Making

Sharon-Lise Normand
Harvard Medical School
January 17, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Studentized Sensitivity Analysis in Paired Observational Studies

Colin Fogarty
MIT
January 17, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Learning to Personalize from Observational Data Under Unobserved Confounding

Nathan Kallus
Cornell University
January 17, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Personalizing Causal Pathway Effects

Ilya Shpitser
Johns Hopkins
January 16, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Extending causal inferences from a randomized trial to a target population

Issa Dahabreh
Brown University
January 16, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract     Slides

Estimating Effects in Clinical Trials Under Unobserved Conditions

Alisa Stephens-Shields
University of Pennsylvania
January 16, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Subgroup Identification using Covariate Adjusted Interaction Trees

Jon Steingrimsson
Brown University
January 16, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Data Efficient Causal Effect Estimation

Adith Swaminathan
Microsoft Research
January 16, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract     Slides

Nonparametric super-efficient estimators of treatment effects

David Benkeser
Emory University
January 16, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Estimating the effects of Meals on Wheels through Linkage to Claims Data Sources

Roee Gutman
Brown University
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Bayesian nonparametrics and g-computation for causal inference in observational studies

Jason Roy
Rutgers University
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Using systems models for causal inference- a data-assumption trade-off

Eleanor Murray
Harvard University SPH
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Challenges of Mediation Analysis in Neuroimaging Studies

Yi Zhao
Johns Hopkins University
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract     Slides

Microsimulation Models in Medical Decision Making – Calibration and Predictive Accuracy

Stavroula Chrysanthopoulou
Brown University
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract     Slides

Agent-based models for evaluating HIV prevention modalities in sexual and injecting networks

Brandon Marshall
Brown University School of Public Health
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Statistical approaches to understanding Tuberculosis transmission dynamics

Laura White
Boston University
January 15, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Bayesian Methods for Multiple Mediators- Relating Principal Stratification and Causal Mediation

Chanmin Kim
Boston University
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Toward Evaluation of Dissemination of HIV Prevention Interventions Among Networks of People who Inject Drugs

Ashley Buchanan
University of Rhode Island
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract     Slides

Causal Inference- Why and How Bayes?

Michael Daniels
University of Florida
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference using Five Empirical Applications

Luke Kelle
University of Pennsylvania
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Causal inference from complex observational data

Alexander Volfovsky
Duke University
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

Targeted Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes

Ivan Diaz
Weill Cornell Medicine
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract

(Fast) Rates for Estimating Optimal Individualized Treatment Rules

Alex Luedtke
University of Washington
January 14, 2019
Models and Machine Learning for Causal Inference and Decision Making in...
Play     Abstract