Learning Generated Controls under Fractured Geometry: Projective Residualization and Variation-Allocation Frontiers
Many two-stage estimators assess the first-stage learner by prediction error, even when the next stage uses its residual. In control-function instrumental variables, that residual must preserve the latent control direction without removing the treatment variation that identifies the structural response. A scalar prediction score does not reveal how the learner allocates this variation. Under piecewise-smooth graph geometry, interpolation can suppress the control, whereas isotropic smoothing can leak systematic variation across boundaries. We formulate this as a variation-allocation problem and introduce Adaptive Anisotropic Instrumental Heat Flow (A-IHF). The method uses pilot treatment c...
| Authors | Rui Wu, Zongyuan Chen, Hong Xie et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 June 2026 |
| Last updated at source | 29 August 2026 |
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