Target-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Model
Knowing how much a causal predictor could improve need not reveal the gain of the repair actually learned. We quantify this gap in a scalar Gaussian causal experiment with known intervention geometry: auxiliary data identify effect magnitude up to bounded contamination, while diagnostics identify direction. The target is the squared-loss gain of the realized trained repair relative to a fitted reference. Jointly optimizing the learner and assessor under uniform learning MSE $η$ avoids the trivial solution of making no repair. At the usual $1/k$ learning scale, every feasible learner incurs a $k^{-2}$ assessment floor, even when oracle potential is estimable at a faster rate. In the magnit...
| Authors | Qinchuan Cheng, Jiaqi Liu, Ruixuan Xie |
|---|---|
| Primary category | stat.ML |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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