# Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
## Appendix A
\begin{tabular}{l r r r} \hline \hline
**Task and Sample** & \multicolumn{3}{c}{**Running Time**} \\ \hline Unconstrained tree depth & 2 & 3 & 4 \\ \hline Policy tree training data: & 0:08.334 & 0:29.838 & 10:22.988 \\ Evaluation of training data: & 0:00.172 & 0:00.221 & 0:00.173 \\ Policy tree allocation data: & 0:00.571 & 0:00.528 & 0:00.543 \\ Evaluation of test data: & 0:00.140 & 0:00.115 & 0:00.165 \\ \hline Unconstrained sequential trees depth & 2+1 & 3+1 & 4+1 \\ \hline Policy tree training data: & 0:7.667 & 0:25.610 & 5:53.018 \\ Evaluation of training data: & 0:0.236 & 0:0.189 & 0:0.220 \\ Policy tree allocation data: & 0:1.670 & 0:1.733 & 0:1.472 \\ Evaluation of test data: & 0:0.157 & 0:0.157 & 0:0.110 \\ \hline Unconstrained sequential trees depth & 2+2 & 3+2 & 4+2 \\ \hline Policy tree training data: & 0:09.591 & 0:25.590 & 3:45.807 \\ Evaluation of training data: & 0:00.252 & 0:00.284 & 0:00.236 \\ Policy tree allocation data: & 0:01.887 & 0:02.141 & 0:02.010 \\ Evaluation of test data: & 0:00.157 & 0:00.173 & 0:00.173 \\ \hline \hline \end{tabular}   
Appendix A.3.4. Policy Trees Running Time: Interest Rates and Loan Returns Study  
\begin{tabular}{l r r r} \hline \hline
**Task and Sample** & \multicolumn{3}{c}{**Running Time**} \\ \hline Unconstrained tree depth & 2 & 3 & 4 \\ \hline Policy tree training data: & 0:08.334 & 0:29.838 & 10:22.988 \\ Evaluation of training data: & 0:00.172 & 0:00.221 & 0:00.173 \\ Policy tree allocation data: & 0:00.571 & 0:00.528 & 0:00.543 \\ Evaluation of test data: & 0:00.140 & 0:00.115 & 0:00.165 \\ \hline Unconstrained sequential trees depth & 2+1 & 3+1 & 4+1 \\ \ \begin{tabular}{l c} \hline \hline
**Splitting variables and values** & **Treatment allocation** \\ \hline age \(\leq\) 50, age \(>\) 36 & HR \\ \hline age \(>\) 50, qnt_income \(\leq\) 2 & HR \\ \hline age \(\leq\) 50, age \(\leq\) 36 & MR \\ \hline age \(>\) 50, qnt_income \(>\) 2 & LR \\ \hline \hline \end{tabular}   
Note: Policy rule for individuals assigned to HR: high rate, MR: medium rate, and LR: low rate. \begin{tabular}{l c} \hline \hline
**Splitting variables and values** & **Treatment allocation** \\ \hline age \(>\) 46, qnt_income \(>\) 2, age \(>\) 50 & LR \\ \hline age \(\leq\) 46, age \( \begin{table}
\begin{tabular}{l c} \hline \hline
**Splitting variables and values** & **Treatment allocation** \\ \hline age \(>\) 43, qnt\_income \(>\) 2, age \(>\) 46, age \(>\) 50 & LR \\ \hline age \(\leq\) 43, age \(\leq\) 33, age \(>\) 28, age \(>\) 32 & LR \\ \hline age \(\leq\) 43, age \(>\) 33, edhi = 0, age \(\leq\) 35 & LR \\ \hline age \(>\) 43, qnt\_income \(>\) 2, age \(\leq\) 46, female = 1 & LR \\ \hline age \(\leq\) 43, age \(\leq\) 33, age \(\leq\) 28 female = 1 & MR \\ \hline age \(\leq\) 43, age \(\leq\) 33, age \(>\) 28, age \(\leq\) 32 & MR \\ \hline age \(\leq\) 43, age \(>\) 33, edhi =1, age \(\leq\) 37 & MR \\ \hline age \(>\) 43, qnt\_income \(\leq\) 2, age \(\leq\) 50, female = 0 & MR \\ \hline age \(>\) 43, qnt\_income \(\leq\) 2, age \(>\) 50, age \(\leq\) 53 & MR \\ \hline age \(\leq\) 43, age \(\leq\) 33, age \(\leq\) 28, female = 0 & HR \\ \hline age \(\leq\) 43, age \(>\) 33, edhi = 0, age \(>\) 35 & HR \\ \hline age \(\leq\) 43, age \(>\) 33, edhi = 1, age \(>\) 37 & HR \\ \hline age \(>\) 43, qnt\_income \(\leq\) 2, age \(\leq\) 50, female = 1 & HR \\ \hline age \(>\) 43, qnt\_income \(\leq\) 2, age \(>\) 50, age \(>\) 53 & HR \\ \hline age \(>\) 43, qnt\_income \(>\) 2, age \(\leq\) 46, female = 0 & HR \\ \hline age \(>\) 43, qnt\_income \(>\) 2, age \(>\) 46, age \(\leq\) 50 & HR \\ \hline \hline \end{tabular} Note: Policy rule for individuals assigned to HR: high rate, MR: medium rate, and LR: low rate.  
\end{table} 
Table 19: _Cont._