TE: Functions for estimation of treatment effects In hdm: High-Dimensional Metrics

Description

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).

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37``` ```rlassoATE(x, ...) ## Default S3 method: rlassoATE(x, d, y, bootstrap = "none", nRep = 500, ...) ## S3 method for class 'formula' rlassoATE(formula, data, bootstrap = "none", nRep = 500, ...) rlassoATET(x, ...) ## Default S3 method: rlassoATET(x, d, y, bootstrap = "none", nRep = 500, ...) ## S3 method for class 'formula' rlassoATET(formula, data, bootstrap = "none", nRep = 500, ...) rlassoLATE(x, ...) ## Default S3 method: rlassoLATE(x, d, y, z, bootstrap = "none", nRep = 500, post = TRUE, intercept = TRUE, ...) ## S3 method for class 'formula' rlassoLATE(formula, data, bootstrap = "none", nRep = 500, post = TRUE, intercept = TRUE, ...) rlassoLATET(x, ...) ## Default S3 method: rlassoLATET(x, d, y, z, bootstrap = "none", nRep = 500, post = TRUE, intercept = TRUE, ...) ## S3 method for class 'formula' rlassoLATET(formula, data, bootstrap = "none", nRep = 500, post = TRUE, intercept = TRUE, ...) ```

Arguments

 `x` exogenous variables `...` arguments passed, e.g. `intercept` and `post` `d` treatment variable (binary) `y` outcome variable / dependent variable `bootstrap` boostrap method which should be employed: 'none', 'Bayes', 'normal', 'wild' `nRep` number of replications for the bootstrap `formula` An object of class `Formula` of the form " y ~ x + d | x" with y the outcome variable, d treatment variable, and x exogenous variables. `data` 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. `z` instrumental variables (binary) `post` logical. If `TRUE`, post-lasso estimation is conducted. `intercept` logical. If `TRUE`, intercept is included which is not penalized.

Details

Details can be found in Belloni et al. (2014).

Value

Functions return an object of class `rlassoTE` with estimated effects, standard errors and individual effects in the form of a `list`.

References

A. Belloni, V. Chernozhukov, I. Fernandez-Val, and C. Hansen (2014). Program evaluation with high-dimensional data. Working Paper.

hdm documentation built on May 29, 2017, 4:28 p.m.