Organizing Committee: Michael Sobel, Jose Zubizaretta, Andrew Gelman

DATE: November 10-12

LOCATION: Thursday, November 10: Uris 326 – Friday, November 11: Warren 207 – Saturday, November 12: Uris 141

Conference Program: Pdf

Registration is currently closed. Please check back next week.

The conference will also be streamed live for those unable to register and attend.

Streaming Links

Nov 10th 2:15- 5:30pm

https://cbs360.gsb.columbia.edu:8443/ess/echo/presentation/58addaa7-3f7f-432d-a112-7f08ac82baea

Nov 11th 9am- 1pm

https://cbs360.gsb.columbia.edu:8443/ess/echo/presentation/183aea89-c36c-4bc9-b28c-18318cd596a9

Nov 11th 1pm- 5pm

https://cbs360.gsb.columbia.edu:8443/ess/echo/presentation/715be634-dcf0-455b-950a-74b750e2f7d4

Nov 12th 9am- 12pm

https://cbs360.gsb.columbia.edu:8443/ess/echo/presentation/a50b7883-bf4d-4abf-8e32-ccfcd5dbc1d9

Nov 12th 12pm- 2pm

https://cbs360.gsb.columbia.edu:8443/ess/echo/presentation/88edcd1c-7295-40cf-87a4-b5ff0afd2baf

Nov 12th 2pm- 5:30pm

https://cbs360.gsb.columbia.edu:8443/ess/echo/presentation/8378cfbe-9781-4bcb-84be-4955c8e36220

Our first conference on causal inference will focus on ways to estimate a number of different types of treatment effects both when standard assumptions such as ignorability and SUTVA hold, as well as cases where these assumptions fail.

      SPEAKERS

1. Edoardo Airoldi: Harvard University
2. Joshua Angrist: Massachusetts Institute of Technology
3. Gary Chan: University of Washington
4. David Choi: Carnegie Mellon University
5. Dean Eckles: Massachusetts Institute of Technology
6. Michael Hudgens: University of North Carolina, Chapel Hill
7. Fan Li: Duke University
8. Jamie Robins: Harvard University
9. Sherri Rose: Harvard University
10. Paul Rosenbaum: University of Pennsylvania
11. Donald Rubin: Harvard University
12. Dylan Small: University of Pennsylvania
13. Mark van der Laan: University of California, Berkeley
14. Stefan Wager: Columbia University
15. Xiaoru Wu: Facebook
16. Cunhui Zhang: Rutgers University

Conference Program

Thursday, November 10, 2016: tutorial       

Targeted Learning

This course will introduce targeted learning methods for causal inference. It will emphasize understanding and responding to the challenges posed by observational cohorts and randomized trials including high-dimensional “big data.” Examples from the areas of health policy, medicine, and epidemiology will be used as illustrations to translate research questions into statistical estimation problems with accurate interpretation of results. Course content covers material from Chapters 1-6 of “Targeted Learning” by van der Laan & Rose, as well as additional advances.

2:15        3:00        Sherri Rose/Mark vander Laan

3:00        3:45        Sherri Rose/Mark vander Laan

3:45        4:00        (Coffee break)

4:00        4:45        Sherri Rose/Mark vander Laan

4:45        5:30        Sherri Rose/Mark vander Laan

Friday, November 11, 2016: talks                             

9:00        9:15        Opening remarks

9:15        10:00     Donald Rubin

10:00     10:20     (Coffee break)

10:20     11:05     Stefan Wager

11:05     11:50     Mark vander Laan

11:50     1:20        (Lunch break)

1:20        2:05        Xiaoru Wu

2:05        2:50        Cunhui Zhang

2:50        3:10        (Coffee break)

3:10        3:55        Michael Hudgens

3:55        4:40        David Choi

5:00        6:00        Reception in the Department of Statistics

6:30                        (Dinner for speakers)

Saturday, November 12, 2016: talks                        

9:00        9:45        Fan Li

9:45        10:30     Paul Rosenbaum

10:30     10:50     (Coffee break)

10:50     11:35     Joshua Angrist

11:35     12:20     Dylan Small

12:20     1:50        (Lunch break)

1:50        2:35        Jamie Robins

2:35        3:20        Gary Chan

3:20        3:40        (Coffee break)

3:40        4:25        Edo Airoldi

4:25        5:10        Dean Eckles