Package: hdcate 0.1.0

Qingliang Fan

hdcate: Estimation of Conditional Average Treatment Effects with High-Dimensional Data

A two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the “double robustness” property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics <doi:10.1080/07350015.2020.1811102>.

Authors:Qingliang Fan [aut, cre], Hengzhao Hong [aut]

hdcate_0.1.0.tar.gz
hdcate_0.1.0.tar.gz(r-4.7-any)hdcate_0.1.0.tar.gz(r-4.6-any)
hdcate_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
hdcate/json (API)

# Install 'hdcate' in R:

install.packages('hdcate', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 5 scripts 312 downloads 11 exports 80 dependencies

Last updated from:af3046090c. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	176
source / vignettes	OK	251
linux-release-x86_64	OK	169
wasm-release	OK	164

Exports:HDCATE HDCATE.fit HDCATE.get_sim_data HDCATE.inference HDCATE.plot HDCATE.set_bw HDCATE.set_condition_var HDCATE.set_first_stage HDCATE.unset_first_stage HDCATE.use_cross_fitting HDCATE.use_full_sample

Dependencies:backports caret checkmate class cli clock codetools cpp11 data.table diagram digest dplyr e1071 farver foreach Formula future future.apply generics ggplot2 glmnet globals glue gower gtable hardhat hdm ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv locpol lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp RcppEigen recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

User Manual: High-Dimensional Conditional Average Treatment Effects Estimation (R Package)

Rendered fromuser_manual.Rmdusingknitr::rmarkdown

Last update: 2022-12-14
Started: 2022-12-14

Help page	Topics
High-Dimensional Conditional Average Treatment Effects (HDCATE) Estimator	HDCATE
Fit the HDCATE function	HDCATE.fit
Get simulation data	HDCATE.get_sim_data
Construct uniform confidence bands	HDCATE.inference
Plot HDCATE function and the uniform confidence bands	HDCATE.plot
Set bandwidth	HDCATE.set_bw
Set the conditional variable in CATE	HDCATE.set_condition_var
Set user-defined first-stage estimating methods	HDCATE.set_first_stage
Clear the user-defined first-stage estimating methods	HDCATE.unset_first_stage
Use k-fold cross-fitting estimator	HDCATE.use_cross_fitting
Use full-sample estimator	HDCATE.use_full_sample

Package: hdcate 0.1.0

hdcate: Estimation of Conditional Average Treatment Effects with High-Dimensional Data

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Help Manual

Usage by other packages (reverse dependencies)