Online Causal Inference Seminar

Upcoming Seminars

All seminars are on Tuesdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 5:30 pm Berlin / 11:30 pm Beijing.

You can join the webinar on Zoom here (webinar ID: 968 8371 7451). The password is 414559. Past talks are available here.

• Tuesday, Mar 25, 2025: Guido Imbens (Stanford University)
  - Title: Identification of nonparametric factor models for average treatment effects
  - Discussant: Bryan Graham (UC Berkeley)
  - Abstract: There is a growing literature on methods for estimating causal effects in settings with panel or longitudinal data using two-way-fixed-effect, linear factor, and synthetic control methods. These methods attempt to adjust for unobserved differences between units as well as unobserved differences over time. Many of these methods partly rely on functional form assumptions to allow for such adjustments. Here we propose a set up that does not involve functional form assumptions. We show that by matching units on a similarity distance we can find units that are comparable in terms of expected outcomes and use those units to consistently estimate average causal effects under a matrix version of standard ignorability conditions.
  [Slides][Video][Discussant slides]

• Tuesday, Apr 01, 2025: Dylan Small (University of Pennsylvania)
  - Title:  Exploration, Confirmation, and Replication in the Same Observational Study: A Two Team Cross-Screening Approach to Studying the Effect of Unwanted Pregnancy on Mothers’ Later Life Outcomes
  - Discussant:  Ying Jin (Harvard University)
  - Abstract: The long term consequences of unwanted pregnancies carried to term on mothers have not been much explored. We use data from the Wisconsin Longitudinal Study (WLS) and propose a novel approach, namely two team cross-screening, to study the possible effects of unwanted pregnancies carried to term on various aspects of mothers’ later-life mental health, physical health, economic well-being and life satisfaction. Our method, unlike existing approaches to observational studies, enables the investigators to perform exploratory data analysis, confirmatory data analysis and replication in the same study– this is a valuable property when there is only a single data set available with unique strengths to perform exploratory, confirmatory and replication analysis. In two team cross-screening, the investigators split themselves into two teams and the data is split as well according to a meaningful covariate. Each team then performs exploratory data analysis on its part of the data to design an analysis plan for the other part of the data. The complete freedom of the teams in designing the analysis has the potential to generate new unanticipated hypotheses in addition to a prefixed set of hypotheses. Moreover, only the hypotheses that looked promising in the data each team explored are forwarded for analysis (thus alleviating the multiple testing problem). These advantages are demonstrated in our study of the effects of unwanted pregnancies on mothers’ later life outcomes.  This is joint work with Samrat Roy, Marina Bogomolov, Ruth Heller, Tishra Beeson and Amy Claridge.
  [Paper]

• Tuesday, Apr 08, 2025: Nathan Kallus (Cornell University)

• Tuesday, Apr 15, 2025 [SCI+OCIS]: Gabriel Loewinger (NIH), Emre Kiciman (Microsoft), Natalie Levy (Aetion)Ga
  - Title:  Roundtable Panel – Exploring Career Paths in Pharma, Government, and Technology

• Tuesday, May 06, 2025: Zijian Guo (Rutgers University)

• Tuesday, May 13, 2025: Sam Pimentel (UC Berkeley)

• Tuesday, June 03, 2025: Edward Kennedy (Carnegie Mellon University)

briel Loewinger, PhD (NIH), Emre Kiciman, PhD (Microsoft), Natalie Levy, PhD (Aetion)