Doubly Robust Average Partial Effects

---

Documentation for package ‘drape’ version 0.0.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

basis_poly	Estimate the score function of the d'th covariate using a polynomial basis.
compare	Generate simulation data and evaluate estimators, with sample splitting.
compare_evaluate	Evaluate estimators by training nuisance functions on training set and evaluating them on test set.
compare_lm	Generate simulation data and evaluate OLS estimator.
compare_partially_linear	Generate simulation data and evaluate partially linear estimator.
compare_rothenhausler	Generate simulation data and evaluate Rothenhausler estimator.
cv_resmooth	K-fold cross-validation for resmoothing bandwidth.
cv_spline_score	K-fold cross-validation for spline_score.
drape	Estimate the doubly-robust average partial effect estimate of X on Y, in the presence of Z.
fit_lasso_poly	Fit a lasso regression using quadratic polynomial basis, with interactions.
fit_xgboost	Fit pre-tuned XGBoost regression for use in simulations.
MC_sums	Compute sums of a Monte Carlo vector for use in resmoothing.
mixture_score	Population score function for the symmetric mixture two Gaussian random variables.
new_X	Generate a matrix of covariates for use in resmoothing, in which the d'th column of X is translated successively by the Kronecker product of bw and MC_variates.
ng_pseudo_response	Generate pseudo responses as in Ng 1994 to enable univariate score estimation by standard smoothing spline regression.
partially_linear	Fit a doubly-robust partially linear regression using the DoubleML package and pre-tuned XGBoost regressions, for use in simulations.
resmooth	Resmooth the predictions of a fitted model
rmixture	Symmetric mixture two Gaussian random variables.
rothenhausler_basis	Generate the modified quadratic basis of Rothenhausler and Yu.
rothenhausler_yu	Estimate the average partial effect of using the debiased lasso method of Rothenhausler and Yu, using pre-tuned lasso penalties, for use in simulations.
simulate_data	Generate simulation data.
sort_bin	Sort and bin x within a specified tolerance, using hist().
spline_score	Univariate score estimation via the smoothing spline method of Cox 1985 and Ng 1994.
z_correlated_normal	Generate n copies of Z ~ N_p(0,Sigma), where Sigma_jj = 1, Sigma_jk = corr for all j not equal to k.