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. You can model continuous, binary, and count outcomes and choose between a logit or probit treatment model. You can estimate the average treatment effect, the average treatment effect on the treated, and the potential-outcome means. With telasso, you get everything you expect from treatment effects and from lasso. The obtained estimates benefit from robustness properties of both the treatment-effects estimators and lasso. 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. With the new telasso command, you can estimate treatment effects while controlling for many potential covariates. You can now use these estimators simultaneously. (And when we say many, we mean hundreds, thousands, or more!) You use lasso inferential estimators when you are interested in inference on a few covariates while controlling for many other potential covariates. 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 treatment-effects estimators to draw causal inferences from observational data. Neyman orthogonality: guard against model-selection mistakes made by lasso.Double robustness: only one of the models needs to be correctly specified.ATET: average treatment effect on the treated.Different measures of treatment effects.Treatment assignment model can be logit or probit.Outcome model can be linear, logit, probit, or poisson.High-dimensional controls in the treatment model.High-dimensional controls in the outcome model.Estimate treatment effects with high-dimensional controls.
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