# Assumption-Lean and Data-Adaptive Post-Prediction Inference

arXiv: https://arxiv.org/abs/2311.14220 · HTML: https://mlsanity.com/paper/2311.14220/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [PPI++: Efficient Prediction-Powered Inference](https://mlsanity.com/paper/2311.01453/) (2023)
- [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)

## Built on by (most cited first, 26 of 26)

- [Active Statistical Inference](https://mlsanity.com/paper/2403.03208/) (2024)
- [Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI](https://mlsanity.com/paper/2501.09731/) (2025)
- [This human study did not involve human subjects: Validating LLM simulations as behavioral evidence](https://mlsanity.com/paper/2602.15785/) (2026)
- [Prediction De-Correlated Inference: A safe approach for post-prediction inference](https://mlsanity.com/paper/2312.06478/) (2023)
- [Task-Agnostic Machine-Learning-Assisted Inference](https://mlsanity.com/paper/2405.20039/) (2024)
- [Demystifying Prediction Powered Inference](https://mlsanity.com/paper/2601.20819/) (2026)
- [Multiple-Prediction-Powered Inference](https://mlsanity.com/paper/2603.27414/) (2026)
- [Another look at statistical inference with machine learning-imputed data](https://mlsanity.com/paper/2411.19908/) (2024)
- [A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation](https://mlsanity.com/paper/2508.15162/) (2025)
- [Efficient Inference for Noisy LLM-as-a-Judge Evaluation](https://mlsanity.com/paper/2601.05420/) (2026)
- [Robust Sampling for Active Statistical Inference](https://mlsanity.com/paper/2511.08991/) (2025)
- [Imputation-Powered Inference](https://mlsanity.com/paper/2509.13778/) (2025)
- [Revisiting Active Sequential Prediction-Powered Mean Estimation](https://mlsanity.com/paper/2604.18569/) (2026)
- [High-Dimensional Statistics: Reflections on Progress and Open Problems](https://mlsanity.com/paper/2605.05076/) (2026)
- [Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data](https://mlsanity.com/paper/2512.05456/) (2025)
- [Generalized Prediction-Powered Inference, with Application to Binary Classifier Evaluation](https://mlsanity.com/paper/2602.10332/) (2026)
- [Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research](https://mlsanity.com/paper/2605.29249/) (2026)
- [Do We Really Even Need Data?](https://mlsanity.com/paper/2401.08702/) (2024)
- [On the Role of Surrogates in Conformal Inference of Individual Causal Effects](https://mlsanity.com/paper/2412.12365/) (2024)
- [SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning](https://mlsanity.com/paper/2509.21707/) (2025)
- [MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation](https://mlsanity.com/paper/2604.05446/) (2026)
- [Valid Inference with Synthetic Data via Task Exchangeability](https://mlsanity.com/paper/2606.13629/) (2026)
- [When Can LLM Digital Twins Reduce Human Measurement? From Behavioral Fidelity to Statistical Substitutability](https://mlsanity.com/paper/2609.07987/) (2026)
- [Bias-corrected Cox regression with AI-extracted covariates via calibration summary statistics](https://mlsanity.com/paper/2607.25868/) (2026)
- [Coarsening Latent-Class Probabilities: Directional Distortion and Coverage Loss](https://mlsanity.com/paper/2608.11784/) (2026)
- [Artificial intelligence surrogates for treatment effect estimation with before-and-after data](https://mlsanity.com/paper/2609.27180/) (2026)

## Abstract (arXiv)

A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be costly, labor-intensive, or invasive to obtain. With the rapid development of machine learning (ML), scientists can now employ ML algorithms to predict gold-standard outcomes with variables that are easier to obtain. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data. Its "assumption-lean" property guarantees reliable statistical inference without assumptions on the ML prediction. Its "data-adaptive" feature guarantees an efficiency gain over existing methods, regardless of the accuracy of ML prediction. We demonstrate the statistical superiority and broad applicability of our method through simulations and real-data applications.