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paper_alg1

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