Estimate treatment effects with high-dimensional controls
High-dimensional controls in the outcome model
High-dimensional controls in the treatment model
Flexible model specification
Outcome model can be linear, logit, probit, or poisson
Treatment assignment model can be logit or probit
Different measures of treatment effects
ATE: average treatment effects
ATET: average treatment effect on the treated
POM: potential-outcome mean
Double robustness: only one of the models needs to be correctly specified
Neyman orthogonality: guard against model-selection mistakes made by lasso
Double machine learning
Cross-fitting and resampling
You use treatment-effects estimators to draw causal inferences from observational data. Perhaps you want to estimate the effect of a drug regimen on blood pressure, the effect of a surgical procedure on mobility, the effect of a training program on employment, or the effect of an ad campaign on sales.
You use lasso inferential estimators when you are interested in inference on a few covariates while controlling for many other potential covariates. (And when we say many, we mean hundreds, thousands, or more!)
You can now use these estimators simultaneously. With the telasso command, you can estimate treatment effects while controlling for many potential covariates.
For example, you can type
. telasso (y1 x1-x100) (treat w1-w100)
to estimate the effect of the binary treatment treat on the continuous outcome y1 while controlling for predictors x1 through x100 in the outcome model and for w1 through w100 in the treatment model. The obtained estimates benefit from robustness properties of both the treatment-effects estimators and lasso.
With telasso, you get everything you expect from treatment effects and from lasso. You can estimate the average treatment effect, the average treatment effect on the treated, and the potential-outcome means. You can model continuous, binary, and count outcomes and choose between a logit or probit treatment model. And for selection of controls, you can choose between lasso or square-root lasso estimation and choose from several selection methods, such as BIC and cross-validation.
See more examples and information on telasso in [CAUSAL] telasso.
Learn more about treatment effects in the Stata Causal Inference and Treatment-Effects Estimation Reference Manual.
Learn more about lasso in the Stata Lasso Reference Manual.