Prediction-Powered E-Values

• URL: http://arxiv.org/abs/2502.04294v1
• Date: Thu, 06 Feb 2025 18:36:01 GMT
• Title: Prediction-Powered E-Values
• Authors: Daniel Csillag, Claudio José Struchiner, Guilherme Tegoni Goedert,
• Abstract summary: We apply ideas of prediction-powered inference to e-values. We show that every inference procedure that can be framed in terms of e-values has a prediction-powered counterpart. Our approach is modular and easily integrable into existing algorithms.
• Score: 0.66567375919026
• License:
• Abstract: Quality statistical inference requires a sufficient amount of data, which can be missing or hard to obtain. To this end, prediction-powered inference has risen as a promising methodology, but existing approaches are largely limited to Z-estimation problems such as inference of means and quantiles. In this paper, we apply ideas of prediction-powered inference to e-values. By doing so, we inherit all the usual benefits of e-values -- such as anytime-validity, post-hoc validity and versatile sequential inference -- as well as greatly expand the set of inferences achievable in a prediction-powered manner. In particular, we show that every inference procedure that can be framed in terms of e-values has a prediction-powered counterpart, given by our method. We showcase the effectiveness of our framework across a wide range of inference tasks, from simple hypothesis testing and confidence intervals to more involved procedures for change-point detection and causal discovery, which were out of reach of previous techniques. Our approach is modular and easily integrable into existing algorithms, making it a compelling choice for practical applications.

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