This six-hour course shows how to estimate causal parameters from a high-dimensional model using the inferential lasso commands in Stata.
It begins by introducing high-dimensional models and discusses which estimation methods work and which estimation methods do not work. This introduction shows how the lasso is used in these methods.
Next, the course provides an introduction to how the lasso is implemented in Stata and an overview of relevant theory. This part also discusses how to use Stata’s lasso commands to solve prediction problems.
Finally, the course discusses the Stata implementation and the relevant theory for a series of commands that estimate causal parameters from high-dimensional models. These parts of the course discuss commands for linear models, logit models and Poisson models with exogenous variables. They also discuss commands for linear models with endogenous variables. Some extensions to average treatment effects for exogenous treatments are also discussed.
David Drukker, StataCorp
David M. Drukker is the Executive Director of Econometrics at Stata and has a Ph.D. in Economics from the University of Texas at Austin.
He has been with Stata since 1999. He has developed many Stata commands for estimating treatment effects and for analyzing panel data, time-series data, and cross-sectional data. He played a key role in the initial development of Stata MP, helped integrate Mata into Stata, and has helped develop some of Stata’s numerical techniques. David has also published papers on econometric methods and been principal investigator on two large research grants.