Skip to main content
Cornell university
Cornell Statistics and Data Science Cornell Statistics and Data Science
  • About Us

    About Us
    Cornell's Department of Statistics and Data Science offers four programs at the undergraduate and graduate levels. Undergraduates can earn a BA in statistical science, social...

    Welcome to the Department of Statistics and Data Science
    History
    Facilities
    Statistics Graduate Society
    Recently Published Papers
  • Academics

    Academics

    Undergraduate
    PhD
    MPS
    PhD Minor in Data Science
    Courses & Course Enrollment
  • People

    People

    Faculty
    Field Faculty
    PhDs
    Emeritus Faculty
    Academic Staff
    Staff
    Research Areas of Expertise
    Statistical Consultants
  • News and Events

    News and Events

    Events
    News
  • Resources

    Resources

    Professional Societies and Meetings
    Affiliated Groups
    Career Services
    Cornell Statistical Consulting Unit
  • Alumni

    Alumni
    Cornell's Statistics and Data Science degrees prepare students for a wide variety of careers, from academia to industry.  See the After Graduation page for a general overview of...

    Alumni Profiles

Search form

You are here

  1. Home 
  2. Events 
  3. Statistics Seminars

Statistics Seminar Speaker: Linbo Wang, 2/21/2018

Event Layout

Wednesday Feb 21 2018

Statistics Seminar Speaker: Linbo Wang, 2/21/2018

4:15pm @ G01 Biotechnology
In Statistics Seminars

The Statistics Seminar speaker for Wednesday, February 21, 2018, will be Linbo Wang, currently a postdoctoral fellow in the Department of Biostatistics at Harvard University, mentored by Professors Eric Tchetgen Tchetgen and James Robins.  He received his Ph.D. degree in Biostatistics from the University of Washington, and B.S. degree in Statistics from Peking University. His main research interests include causal inference, missing data, graphical models and robust inference in infinite-dimensional models.

Talk: Causal Inference with Unmeasured Confounding: an Instrumental Variable Approach

Abstract: Causal inference is a challenging problem because causation cannot be established from observational data alone. Researchers typically rely on additional sources of information to infer causation from association. Such information may come from powerful designs such as randomization, or background knowledge such as information on all confounders. However, perfect designs or background knowledge required for establishing causality may not always be available in practice. In this talk, I use novel causal identification results to show that the instrumental variable approach can be used to combine the power of design and background knowledge to draw causal conclusions. I also introduce novel estimation tools to construct estimators that are robust, efficient and enjoy good finite sample properties. These methods will be discussed in the context of a randomized encouragement design for a flu vaccine. 

Refreshments will be served following the seminar in 1181 Comstock Hall. 

Event Categories

  • Statistics Seminars
  • Special Events

Image Gallery

Wang, Linbo
  • Home
  • About Us
  • Contact Us
  • Careers
© Cornell University Department of Statistics and Data Science

1198 Comstock Hall, 129 Garden Ave., Ithaca, NY 14853

Social Menu

  • Facebook
  • Twitter
  • YouTube
Cornell Bowers CIS College of Computing and Information Science Cornell CALS ILR School

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact web-accessibility@cornell.edu for assistance.