Opening Workshop: December 9-11, 2019

** Deadline for applications was October 17, 2019 **

Location

This workshop will be held at Penn Pavilion on the campus of Duke University.

Description

Causality is a subject at the frontier of academic debate over research methodology in a wide range of data-based disciplines including statistics, computer science, as well as in social and medical sciences. The “big data” era has brought unprecedented challenges and opportunities for causal inference. This SAMSI program aims to advance causal inference research by bringing together leading mathematical, statistical, computational, and sciences researchers to pursue innovative methodology for causal inference and applications to important real-world problems.

** Notice of Consent **

SAMSI values the proprietary and intellectual property of our participants. The materials presented at our various workshops and programs are in high demand by event participants and the applied mathematics and statistics community that comprise our audience. Therefore, we encourage all of our invited speakers to share their materials, as appropriate, in order to pass along the valuable research that is being done in your field of study and is a focus of this event. In addition, unless SAMSI is give written approval from our speakers we ARE NOT authorized to share the materials presented at this event.

Please click HERE to complete a SAMSI Consent form for this event. SAMSI appreciates your time and willingness to share this valuable content with others and we hope you enjoy this event!

For any questions or concerns about our consent policy, please contact us at: communications@samsi.info


Confirmed speakers for this event include:


  • Schedule and Supporting Media

    Printed Schedule

    Monday, December 9, 2019
    Penn Pavilion, Duke University

    Time Description Speaker Slides
    8:30am Registration
    9:00-9:05am Opening Remarks Greg Forest, Associate Director, SAMSI
    David Banks, Director, SAMSI
    9:10-10:40am Session 1: Machine Learning and High-dimensional
    Bayesian Nonparametric Models for Treatment Effect Heterogeneity: model parameterization, prior Choice, and posterior summarization Jared Murray, University of Texas-Austin
    Experimenting in Equilibrium Stefan Wagner, Stanford University
    Latent Variable Models, Causal Inference, and Sensitivity Analysis Alex D’Amour, Google
    Targeted Learning for Causal Inference Based on Real World Data Mark van der Laan, University of California-Berkeley
    10:40-11:00am BREAK
    11:00am-12:30pm Session 2: Designs of Causal Studies
    Adaptive Design in Surveys and Clinical Trials: similarities, differences and opportunities for cross-fertilization Michael Rosenblum, Johns Hopkins University
    Fisher Randomization Test: A Confidence Distribution Perspective and Applications to Large Experiments Tirthankar Dasgupta, Rutgers University
    Talk to be determined Jose Zubizarreta, Harvard University
    Talk to be determined Dean Eckles, MIT
    12:30-1:30pm LUNCH (on own)
    1:30-3:00pm Session 3: Theory and Foundation
    Optimal Causal Inference with High-Dimensional Discrete Data Ed Kennedy, Carnegie-Mellon University
    On Testing Marginal versus Conditional Independence Thomas Richardson, University of Washington
    From Causal Inference to Gene Regulation Caroline Uhler, MIT
    3:00-3:30pm BREAK
    3:30-5:00pm Session 4: Young Researchers
    Talk to be determined Georgia Papadogeorgou, Duke University
    Model-Free Assessment of Population Overlap in Observational Studies Lihua Lei, University of California-Berkeley
    Inference on Treatment Effects after Model Selection with Application to Subgroup Analysis Jingshen Wang, University of California-Berkeley
    Smooth Extensions to BART for Heterogeneous Treatment Effect Estimation, with Applications to Women’s Healthcare Practice and Policy Jennifer Starling, University of Texas-Austin
    5:00-7:00pm Poster Session and Reception

    Tuesday, December 10, 2019
    Penn Pavilion, Duke University

    Time Description Speaker Slides
    9:00-10:30am Panel 1: Bridging Theory and Practice
    Talk to be determined Elizabeth Stuart, Johns Hopkins University
    Talk to be determined Laine Thomas, Duke University
    Talk to be determined Beth Ann Griffin, RAND
    Talk to be determined Michael Daniels, University of Florida
    10:30-11:00am BREAK
    11:00am-12:30pm Session 5: Applications to Social and Biological Sciences
    Estimating Causal Effects in Studies of Human Brain Function: new Models, methods and estimands Michael Sobel, Columbia University
    RD Applications in Health Services Research Luke Keele, University of Pennsylvania
    Bipartite Causal Inference with Interference for Evaluating Air Pollution Regulations Cory Zigler, University of Texas-Austin
    Synthetic Control and Weighted Event Study Models with Staggered Adoption Avi Feller, University of California-Berkeley
    12:30-1:30pm LUNCH (on own)
    1:30-3:00pm Session 6: Dynamic Treatment Regimes
    New Statistical Learning Methods for Estimating the Optimal Dynamic Treatment Regime Lu Wang, University of Michigan
    When and How to Treat Patients? Yanxuan Xu, Johns Hopkins University
    Some Applications of Reinforcement Learning in Precision Medicine Michael Kosorok, University of North Carolina-Chapel Hill
    Talk to be determined Eric Tchetgen Tchetgen, University of Pennsylvania
    3:00-3:30pm BREAK
    3:30-5:00pm Session 6: Interference and Networks
    Social Network Dependence, the Replication Crisis, and (In)Valid Inference Betsy Ogburn, Johns Hopkins University
    Talk to be determined Guillaume Basse, Stanford University
    Inference in Models of Discrete Choice with Social Interactions Using Network Data Michael Leung, University of Southern California
    Â Heterogeneous Causal Effects under Network Interference Laura Forastiere, Yale University

    Wednesday, December 11, 2019
    Penn Pavilion, Duke University

    Questions: email ci@samsi.info

    Time Description Speaker Slides
    9:00-10:30am Session 7: Unmeasured Confounders and Natural Experiments
    Difference-in-differences: more than meets the eye Laura Hatfeld, Harvard University
    Selecting Subpopulations for Causal Inference in Regression-Discontinuity Studies Fabrizia Mealli, University of Florence
    Talk to be determined Colin Fogarty, MIT
    Bootstrapping Sensitivity Analysis for Inverse Probability Weighting Estimators Qingyuan Zhao, University of Cambridge
    10:30-11:00am BREAK
    11:00am-12:30pm Session 8: Causal Discovery
    Learning Hidden Causal Variables and Relations Kun Zhang, Carnegie Mellon University
    Talk to be determined Peter Spirtes, Carnegie Mellon University
    Talk to be determined Frederick Eberhardt, Cal Tech
    12:30-1:30pm LUNCH (on own)
    1:30-3:00pm Formal Working Groups
    3:00pm Conclude
    3:15pm Shuttle to RDU Airport