Free
1 Hour
Online
Treatment effects estimate the causal effects of a treatment on an outcome. We usually estimate average treatment effects (ATE), but taking an average across a population may not be useful when the effect is heterogeneous. Average treatment effects conditional on a set of variables (CATEs) help us understand heterogeneous treatment effects, and, by construction, are useful to evaluate how different treatment-assignment policies affect different groups in the population.
In this webinar, we will show how to use Stata 19's new command cate to answer questions such as the following:
Di Liu
Principal Econometrician
Di Liu is a Principal Econometrician in the econometric development team at StataCorp LLC. Di is fascinated by writing statistical software for researchers and doing research in both theoretical and applied econometrics. He is the primary developer of some Stata features, including heterogeneous DID, instrumental variable quantile regression, treatment effects estimation using lasso, lasso for prediction, lasso for inference, spatial autoregressive models, heckpoisson, and betareg. He also published research articles in Canadian Journal of Economics, Econometrics Reviews, Empirical Economics, Econometrics and Statistics, and the Stata journal. Di has a PhD degree in economics from Concordia University in Montreal, Canada; an engineer's degree in software engineering and statistics from Polytech'Lille in Lille, France; and master's and bachelor's degrees in computer science from Hohai University in Nanjing, China.
The webinar is free, but you must register to attend. Registrations are limited so register soon. We will send you an email prior to the start with instructions on how to access the webinar.
