Causal Inference with Tree-Based Machine Learning Algorithms
Include the Javascript Used in Shiny
Causal Effect Regression and Estimation Forests (Tree Ensembles)
Compute the "branches" to be drawn for an causalTree object
Intermediate function for causalTree
Intermediate function for causalTree
Causal Effect Regression and Estimation Trees
Intermediate function for causalTree
Intermediate function for causalTree
Clear Temporary Files
Intermediate function for causalTree
estimate causal Tree
Intermediate function for causalTree
Get the Current Working Directory
Getting Distribution in Treatment and Control Groups
Causal Effect Regression and Estimation Trees: One-step honest estimat...
honest re-estimation and change the frame of object using estimation s...
honest re-estimation and change the frame of object using estimation s...
Honest recursive partitioning Tree
Estimate Heterogeneous Treatment Effect via Causal Tree
Estimate Heterogeneous Treatment Effect via Random Forest
Estimate Heterogeneous Treatment Effect via Adjusted Causal Tree
Estimate Heterogeneous Treatment Effect via Adjusted Causal Tree
Visualize the Estimated Results
Visualize the Estimated Results
Intermediate function for causalTree
Caclulate variable importance
Visualize Causal Tree and the Estimated Results
NN Matching in Leaves
Intermediate function for causalTree
Intermediate function for causalTree
Intermediate function for hte_plot_line
Visualize Causal Tree and Treatment Effects via Shiny
Save Javascript Embedded in Shiny App
Save Necessary Files to Run Shiny App
Save CSS File Embedded in Shiny App
Save HTML Index Embedded in Shiny App
Save Shiny Server Temporarily
Save Shiny UI Temporarily
Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package first started as a fork of the 'causalTree' package on 'GitHub' and we greatly appreciate the authors for their extremely useful and free package.