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:
hdcate_0.1.0.tar.gz
hdcate_0.1.0.tar.gz(r-4.5-noble)hdcate_0.1.0.tar.gz(r-4.4-noble)
hdcate_0.1.0.tgz(r-4.4-emscripten)hdcate_0.1.0.tgz(r-4.3-emscripten)
hdcate.pdf |hdcate.html✨
hdcate/json (API)
| # Install 'hdcate' in R: |
| install.packages('hdcate', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:af3046090c. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 07 2024 |
| R-4.5-linux | OK | Sep 07 2024 |
Exports:HDCATEHDCATE.fitHDCATE.get_sim_dataHDCATE.inferenceHDCATE.plotHDCATE.set_bwHDCATE.set_condition_varHDCATE.set_first_stageHDCATE.unset_first_stageHDCATE.use_cross_fittingHDCATE.use_full_sample
Dependencies:backportscaretcheckmateclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachFormulafuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhathdmipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlocpollubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppEigenrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| 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 |