This class of functions estimates the average treatment effect (ATE), the ATE of the tretated (ATET), the local average treatment effects (LATE) and the LATE of the tretated (LATET). The estimation methods rely on immunized / orthogonal moment conditions which guarantee valid post-selection inference in a high-dimensional setting. Further details can be found in Belloni et al. (2014).
rlassoATE(x, ...)# S3 method for default
rlassoATE(x, d, y, bootstrap = "none", nRep = 500, ...)
# S3 method for formula
rlassoATE(formula, data, bootstrap = "none", nRep = 500,
...)
rlassoATET(x, ...)
# S3 method for default
rlassoATET(x, d, y, bootstrap = "none", nRep = 500, ...)
# S3 method for formula
rlassoATET(formula, data, bootstrap = "none", nRep = 500,
...)
rlassoLATE(x, ...)
# S3 method for default
rlassoLATE(x, d, y, z, bootstrap = "none", nRep = 500,
post = TRUE, intercept = TRUE, always_takers = TRUE,
never_takers = TRUE, ...)
# S3 method for formula
rlassoLATE(formula, data, bootstrap = "none", nRep = 500,
post = TRUE, intercept = TRUE, always_takers = TRUE,
never_takers = TRUE, ...)
rlassoLATET(x, ...)
# S3 method for default
rlassoLATET(x, d, y, z, bootstrap = "none", nRep = 500,
post = TRUE, intercept = TRUE, always_takers = TRUE, ...)
# S3 method for formula
rlassoLATET(formula, data, bootstrap = "none", nRep = 500,
post = TRUE, intercept = TRUE, always_takers = TRUE, ...)
Functions return an object of class rlassoTE with estimated effects, standard errors and
individual effects in the form of a list.
exogenous variables
arguments passed, e.g. intercept and post
treatment variable (binary)
outcome variable / dependent variable
boostrap method which should be employed: 'none', 'Bayes', 'normal', 'wild'
number of replications for the bootstrap
An object of class Formula of the form " y ~ x + d | x" with y the outcome variable,
d treatment variable, and x exogenous variables.
An optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.
If not found in data, the variables are taken from environment(formula), typically the environment from which rlassoATE is called.
instrumental variables (binary)
logical. If TRUE, post-lasso estimation is conducted.
logical. If TRUE, intercept is included which is not
option to adapt to cases with (default) and without always-takers. If FALSE, the estimator is adapted to a setting without always-takers.
option to adapt to cases with (default) and without never-takers. If FALSE, the estimator is adapted to a setting without never-takers.
Details can be found in Belloni et al. (2014).
A. Belloni, V. Chernozhukov, I. Fernandez-Val, and C. Hansen (2014). Program evaluation with high-dimensional data. Working Paper.