paper_alg1"
In evalITR: Evaluating Individualized Treatment Rules

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Algorithm 1 Estimating and Evaluating an Individualized Treatment Rule (ITR) using the Same Experimental Data via Cross-Validation

| Steps in Algorithm 1 | Function/object | Output | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:-----------------------------------| | 1. Split data into $K$ random subsets of equal size $\left(\mathbf{Z}1, \cdots, \mathbf{Z}_k\right)$ | caret::createFolds() within estimate_itr() | dataframe | | 2. k $\leftarrow$ 1 | | | | 3. while $k \leq K$ do | for loop in fit_itr() within estimate_itr() | | | 4. $\quad \mathbf{Z}{-k}=\left[\mathbf{Z}1, \cdots, \mathbf{Z}{k-1}, \mathbf{Z}{k+1}, \cdots, \mathbf{Z}_K\right]$ | trainset object | training data | | 5. $\hat{f}{-k}=F\left(\mathbf{Z}{-k}\right)$ | modulized functions for each ML algoritms (e.g., run_causal_forest()) within estimate_itr() | ITR (binary vector) | | 6. $\hat{\tau}_k=\hat{\tau}{\hat{f}{-k}}\left(\mathbf{Z}_k\right)$ | compute_qoi() function within evaluate_itr() | metrics for fold $k$ | | 7. $k \leftarrow k+1$ | | | | 8. end while | | | | 9.return $\hat{\tau}_F=\frac{1}{K} \sum{k=1}^K \hat{\tau}k$, $\widehat{\mathbb{V}\left(\hat{\tau}_F\right)}=v\left(\hat{f}{-1}, \cdots, \hat{f}_{-k}, \mathbf{Z}_1, \cdots, \mathbf{Z}_K\right)$ | PAPEcv() PAPDcv() and getAupecOutput() functions inside compute_qoi() function within evaluate_itr() | averaging the results across folds |