OutcomeWeights (version 0.1.1)

dml_with_smoother: Double ML estimators with outcome smoothers

Description

Existing Double ML implementations are too general to easily extract smoother matrices required to be compatible with the get_forest_weights() method. This motivates yet another Double ML implementation.

Usage

dml_with_smoother(
  Y,
  D,
  X,
  Z = NULL,
  estimators = c("PLR", "PLR_IV", "AIPW_ATE", "Wald_AIPW"),
  smoother = "honest_forest",
  n_cf_folds = 5,
  n_reps = 1,
  ...
)

Value

A list with three entries:

• results: a list storing the results, influence functions, and score functions of each estimator

• NuPa.hat: a list storing the estimated nuisance parameters and the outcome smoother matrices

Arguments

Y

Numeric vector containing the outcome variable.

D

Optional binary treatment variable.

X

Covariate matrix with N rows and p columns.

Z

Optional binary instrumental variable.

estimators

String (vector) indicating which estimators should be run. Current menu: c("PLR","PLR_IV","AIPW_ATE","Wald_AIPW")

smoother

Indicate which smoother to be used for nuisance parameter estimation. Currently only available option "honest_forest" from the grf package.

n_cf_folds

Number of cross-fitting folds. Default is 5.

n_reps

Number of repetitions of cross-fitting. Default is 1.

...

Options to be passed to smoothers.

References

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.

Knaus, M. C. (2024). Treatment effect estimators as weighted outcomes, https://arxiv.org/abs/2411.11559.

Examples

# \donttest{
# Sample from DGP borrowed from grf documentation
n = 200
p = 5
X = matrix(rbinom(n * p, 1, 0.5), n, p)
Z = rbinom(n, 1, 0.5)
Q = rbinom(n, 1, 0.5)
W = Q * Z
tau =  X[, 1] / 2
Y = rowSums(X[, 1:3]) + tau * W + Q + rnorm(n)

# Run outcome regression and extract smoother matrix
# Run DML and look at results
dml = dml_with_smoother(Y,W,X,Z)
results_dml = summary(dml)
plot(dml)

# Get weights
omega_dml = get_outcome_weights(dml)

# Observe that they perfectly replicate the original estimates
all.equal(as.numeric(omega_dml$omega %*% Y), 
          as.numeric(as.numeric(results_dml[,1])))

# The weights can then be passed to the cobalt package for example.
# }