Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions
In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset. We study reliable selection for black-box CATE predictors: selecting candidates whose CATE errors are below a tolerance while controlling the false discovery rate (FDR). Since CATE errors are unobservable, we construct doubly robust proxy errors from pseudo-outcomes; however, naive proxies can lose power under heteroskedasticity because variance overwhelms the reliability signal. We propose Denoised Conformal Alignment, which subtracts an estimated conditiona...
| Authors | Xinyun Lu, Haoang Chi, Zhiheng Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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