San Francisco Bay Area Chapter of American Statistical Association SFASA

Welcome

SFASA, the San Francisco Bay Area Chapter of the American Statistical Association (ASA), is non-profit organization run by dedicated volunteers. 

This is the place where you will find announcements of local statistical events and seminars, Bay Area job listings, and information on local jobseekers and consultants.

Upcoming events:

Annual Symposium

How to integrate AI tech in daily life 

Sunday, February 9th, 2025 at San Jose State University

For more info click here.

Registration link.

Seminars

A common-cause principle for eliminating selection bias in causal estimands through covariate adjustment

Instructor:  Professor Maya Mathu, Stanford University

Date: Friday, January 24th, 2024

Time: 12-1pm PT

Registration: Link to registration site

Abstract:

Average treatment effects (ATEs) may be subject to selection bias when they are estimated among only a non-representative subset of the target population. Selection bias can sometimes be eliminated by conditioning on a “sufficient adjustment set” of covariates, even for some forms of missingness not at random (MNAR). Without requiring full specification of the causal structure, we consider sufficient adjustment sets to allow nonparametric identification of conditional ATEs in the target population. Covariates in the sufficient set may be collected among only the selected sample. We establish that if a sufficient set exists, then the set consisting of common causes of the outcome and selection, excluding the exposure and its descendants, also suffices. We establish simple graphical criteria for when a sufficient set will not exist, which could help indicate whether this is plausible for a given study. Simulations considering selection due to missing data indicated that sufficiently-adjusted complete-case analysis (CCA) can considerably outperform multiple imputation under MNAR and, if the sample size is not large, sometimes even under missingness at random.