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)?

Confirmed Speakers & Participants

  • Speaker
  • Poster Presenter
  • 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 - 8:55Registration - ICERM 121 South Main Street, Providence RI 0290311th Floor Collaborative Space 
8:55 - 9:00Welcome - ICERM Director11th Floor Lecture Hall 
9:00 - 9:30Workshop Overview - Joe Hogan, Brown University11th Floor Lecture Hall 
9:30 - 10:00(Fast) Rates for Estimating Optimal Individualized Treatment Rules - Alex Luedtke, University of Washington11th Floor Lecture Hall
10:15 - 10:45Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes - Ivan Diaz, Weill Cornell Medicine11th Floor Lecture Hall
11:00 - 11:15Coffee/Tea Break11th Floor Collaborative Space 
11:15 - 11:45Machine learning methods for causal inference from complex observational data - Alexander Volfovsky, Duke University11th Floor Lecture Hall
12:00 - 12:30Comparing 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 - 2:00Break for Lunch / Free Time  
2:00 - 2:30Causal Inference- Why Bayes - Michael Daniels, University of Florida11th Floor Lecture Hall
2:45 - 3:15Toward 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 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:30Bayesian Methods for Multiple Mediators- Relating Principal Stratification and Causal Mediation - Chanmin Kim, Boston University School of Public Health11th Floor Lecture Hall
4:30 - 4:45Discussion 11th Floor Lecture Hall 
4:45 - 6:15Welcome Reception11th Floor Collaborative Space 
Tuesday, January 15, 2019
TimeEventLocationMaterials
9:00 - 9:30Statistical approaches to understanding Tuberculosis transmission dynamics - Laura White, Boston University11th Floor Lecture Hall
9:45 - 10:15Agent-Based Models for Evaluating HIV Prevention Modalities in Sexual and Injecting Networks - Brandon Marshall, Brown University11th Floor Lecture Hall
10:30 - 10:45Coffee/Tea Break11th Floor Collaborative Space 
10:45 - 11:15Microsimulation Models in Medical Decision Making – Calibration and Predictive Accuracy - Stavroula Chrysanthopoulou, Brown University11th Floor Lecture Hall
11:30 - 12:00Challenges of Mediation Analysis in Neuroimaging Studies - Yi Zhao, Johns Hopkins University11th Floor Lecture Hall
12:00 - 12:15Discussion 11th Floor Lecture Hall 
12:15 - 2:00Break for Lunch / Free Time  
2:00 - 2:30Using systems models for causal inference- a data-assumption trade-off - Eleanor Murray, Harvard TH Chan School of Public Health11th Floor Lecture Hall
2:45 - 3:15Bayesian nonparametrics and g-computation for causal inference in observational studies - Jason Roy, Rutgers School of Public Health11th Floor Lecture Hall
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:30Multiple 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 - 4:45Discussion 11th Floor Lecture Hall 
Wednesday, January 16, 2019
TimeEventLocationMaterials
9:00 - 9:30Nonparametric super-efficient estimators of treatment effects - David Benkeser, Emory University11th Floor Lecture Hall
9:45 - 10:15Data Efficient Causal Effect Estimation - Adith Swaminathan, Microsoft Research11th Floor Lecture Hall
10:30 - 11:00Coffee/Tea Break11th Floor Collaborative Space 
11:00 - 11:30Subgroup Identification using Covariate Adjusted Interaction Trees - Jon Steingrimsson, Brown University11th Floor Lecture Hall
11:45 - 12:00Discussion 11th Floor Lecture Hall 
12:00 - 12:15Group Photo11th Floor Lecture Hall 
12:15 - 2:00Break for Lunch / Free Time  
2:00 - 2:30Estimating Treatment Effects in Clinical Trials Under Unobserved Conditions - Alisa Stephens Shields, University of Pennsylvania11th Floor Lecture Hall
2:45 - 3:15Extending causal inferences from randomized trials to a target population - Issa Dahabreh, Brown University11th Floor Lecture Hall
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:30Personalizing Causal Pathway Effects - Ilya Shpitser, Johns Hopkins University11th Floor Lecture Hall
4:30 - 4:45Discussion 11th Floor Lecture Hall 
Thursday, January 17, 2019
TimeEventLocationMaterials
9:00 - 9:30Learning to Personalize from Observational Data Under Unobserved Confounding - Nathan Kallus, Cornell University & Cornell Tech11th Floor Lecture Hall
9:45 - 10:15Studentized Sensitivity Analysis in Paired Observational Studies - Colin Fogarty, Massachusetts Institute of Technology11th Floor Lecture Hall
10:30 - 10:45Coffee/Tea Break11th Floor Collaborative Space 
10:45 - 11:15Causal Inference, Uncertainty, and Health Policy Decision Making - Sharon-Lise Normand, Harvard Medical School11th Floor Lecture Hall
11:30 - 12:00Using incomplete electronic health record data to inform decision making in HIV and AIDS - Giorgos Bakoyannis, Indiana University11th Floor Lecture Hall
12:00 - 12:30Discussion, Wrap up of scientific program 11th Floor Lecture Hall 
12:30 - 2:00Break for Lunch / Free Time  
2:00 - 3:30Collaborative Discussions 11th Floor Lecture Hall 
3:30 - 4:00Coffee/Tea Break11th Floor Collaborative Space 
4:00 - 4:45Collaborative Discussions 11th Floor Lecture Hall 

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Lecture Videos