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Inference Methods for Treatment Effect Estimators.

Source: R/ddml_ate.R, R/ddml_att.R, R/ddml_late.R
summary.ddml_ate.Rd

Inference methods for treatment effect estimators. By default, standard errors are heteroskedasiticty-robust. If the ddml estimator was computed using a cluster_variable, the standard errors are also cluster-robust by default.

Usage

# S3 method for class 'ddml_ate'
summary(object, ...)

# S3 method for class 'ddml_att'
summary(object, ...)

# S3 method for class 'ddml_late'
summary(object, ...)

Arguments

object

An object of class ddml_ate, ddml_att, and ddml_late, as fitted by ddml_ate(), ddml_att(), and ddml_late(), respectively.

...

Currently unused.

Value

A matrix with inference results.

Examples

# Construct variables from the included Angrist & Evans (1998) data
y = AE98[, "worked"]
D = AE98[, "morekids"]
X = AE98[, c("age","agefst","black","hisp","othrace","educ")]

# Estimate the average treatment effect using a single base learner, ridge.
ate_fit <- ddml_ate(y, D, X,
                    learners = list(what = mdl_glmnet,
                                    args = list(alpha = 0)),
                    sample_folds = 2,
                    silent = TRUE)
#> Warning: : 1 propensity scores were trimmed.
summary(ate_fit)
#> ATE estimation results: 
#>  
#>   Estimate Std. Error t value Pr(>|t|)
#>     -0.149     0.0157   -9.49 2.24e-21