# Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction

arXiv: https://arxiv.org/abs/2105.01264 · HTML: https://mlsanity.com/paper/2105.01264/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 16 of 16)

- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Semi-supervised Inference: General Theory and Estimation of Means](https://mlsanity.com/paper/1606.07268/) (2016)
- [On the role of surrogates in the efficient estimation of treatment effects with limited outcome data](https://mlsanity.com/paper/2003.12408/) (2020)
- [Efficient and Adaptive Linear Regression in Semi-Supervised Settings](https://mlsanity.com/paper/1701.04889/) (2017)
- [A unifying approach for doubly-robust ℓ1 regularized estimation of causal contrasts](https://mlsanity.com/paper/1904.03737/) (2019)
- [Semi-supervised Inference for Explained Variance in High-dimensional Linear Regression and Its Applications](https://mlsanity.com/paper/1806.06179/) (2018)
- [Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models](https://mlsanity.com/paper/1805.06970/) (2018)
- [Imputation for High-Dimensional Linear Regression](https://mlsanity.com/paper/2001.09180/) (2020)
- [Linear Hypothesis Testing in Dense High-Dimensional Linear Models](https://mlsanity.com/paper/1610.02987/) (2016)
- [High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework](https://mlsanity.com/paper/1911.11345/) (2019)
- [Inference for the Case Probability in High-dimensional Logistic Regression](https://mlsanity.com/paper/2012.07133/) (2020)
- [Significance testing in non-sparse high-dimensional linear models](https://mlsanity.com/paper/1610.02122/) (2016)
- [Pivotal Estimation via Self-Normalization for High-Dimensional Linear Models with Error in Variables](https://mlsanity.com/paper/1708.08353/) (2017)
- [Optimal Statistical Inference for Individualized Treatment Effects in High-dimensional Models](https://mlsanity.com/paper/1904.12891/) (2019)
- [Robust and Efficient Semi-Supervised Estimation of Average Treatment Effects with Application to Electronic Health Records Data](https://mlsanity.com/paper/1804.00195/) (2018)
- [Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping](https://mlsanity.com/paper/2003.11744/) (2020)

## Built on by (most cited first, 6 of 6)

- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [Prediction De-Correlated Inference: A safe approach for post-prediction inference](https://mlsanity.com/paper/2312.06478/) (2023)
- [Semi-Supervised Risk Control via Prediction-Powered Inference](https://mlsanity.com/paper/2412.11174/) (2024)
- [Coupled Training with Privileged Information and Unlabeled Data](https://mlsanity.com/paper/2605.23268/) (2026)
- [Latent Factor Point Processes for Patient Representation in Electronic Health Records](https://mlsanity.com/paper/2508.20327/) (2025)
- [Inferential Evaluation of Surrogate-Derived Models under Covariate Shift](https://mlsanity.com/paper/2608.15783/) (2026)

## Abstract (arXiv)

Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors. In this paper, we develop a surrogate assisted semi-supervised-learning (SAS) approach to risk modeling with high dimensional predictors, leveraging a large unlabeled data on candidate predictors and surrogates of outcome, as well as a small labeled data with annotated outcomes. The SAS procedure borrows information from surrogates along with candidate predictors to impute the unobserved outcomes via a sparse working imputation model with moment conditions to achieve robustness against mis-specification in the imputation model and a one-step bias correction to enable interval estimation for the predicted risk. We demonstrate that the SAS procedure provides valid inference for the predicted risk derived from a high dimensional working model, even when the underlying risk prediction model is dense and the risk model is mis-specified. We present an extensive simulation study to demonstrate the superiority of our SSL approach compared to existing supervised methods. We apply the method to derive genetic risk prediction of type-2 diabetes mellitus using a EHR biobank cohort.