Table 1.
Marginal mean outcomes obtained from minimum impurity decision assignments, two regression-based methods, and random guessing. Data are generated from binary treatment examples with a sample size of n =250; reported values are based on 1000 Monte Carlo replications, using a test set of size 10 000
| Model | p | MIDAs | QLin | SVR | Random |
|---|---|---|---|---|---|
| (4) | 10 | 9·86 | 9·31 | 9·39 | 7·76 |
| (4) | 25 | 9·87 | 9·20 | 9·17 | 7·76 |
| (4) | 50 | 9·88 | 9·03 | 8·80 | 7·76 |
| (5) | 10 | 9·77 | 9·55 | 9·53 | 7·76 |
| (5) | 25 | 9·76 | 9·44 | 9·36 | 7·76 |
| (5) | 50 | 9·77 | 9·21 | 8·94 | 7·76 |
| (6) | 10 | 9·15 | 9·74 | 9·60 | 7·76 |
| (6) | 25 | 9·14 | 9·51 | 9·35 | 7·76 |
| (6) | 50 | 9·15 | 9·32 | 8·99 | 7·76 |
MIDAs, minimum impurity decision assignments; QLin, Q-learning with a linear model; SVR, Q-learning with support vector regression; Random, random guessing.