Multiply Robust Conformal Risk Control with Coarsened Data
Read the original on arXiv Statistics ML →The paper introduces two new methods for conformal prediction when training data are coarsened or partially missing. Robust Split Prediction (RSP) extends doubly robust estimating equations to general coarsening patterns, producing multiply robust prediction sets. Robust Split Conformal Prediction (RSCP) combines semiparametric theory with conformal risk control by fixing a preliminary threshold estimate to restore monotonicity, enabling higher‑order bias‑controlled coverage guarantees. The authors provide theoretical results for various missingness scenarios and demonstrate the methods with simulations and a rheumatoid‑arthritis study.
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