Workshop in Biostatistics

Medical School Office Building (MSOB)
Rm x303

DATE: January 11, 2018
TIME: 1:30 - 2:50 pm
TITLE: Matching, Balancing, and Inverse-Propensity Weighting
Stefan Wager
Assistant Professor of Operations, Information and Technology, Graduate School of Business and,
by courtesy, of Statistics, Stanford



Variants of matching and weighting are frequently used to adjust for confounders when estimating average treatment effects in observational studies. In this talk, I'll first briefly review pros and cons of state-of-the-art methods for average treatment effect estimation. I'll then show how some limitations of these methods can be addressed by using numerical optimization tools to directly bound minimax error, and discuss both formal (asymptotic) and empirical advantages of the resulting approach.

For background reading on average treatment effect estimation, see:


The talk will draw from the preprint: