2024/08/12 by Ronan Perry, Zichun Xu, Perry, Ronan +5
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2408.06323
openalex publication_date 2024/08/12 · openalex created_date 2024/09/10 · openalex updated_date 2026/07/28
In recent years, there has been substantial interest in the task of selective inference: inference on a parameter that is selected from the data. Many of the existing proposals fall into what we refer to as the infer-and-widen framework: they produce symmetric confidence intervals whose midpoints do not account for selection and therefore are biased; thus, the intervals must be wide enough to account for this bias. In this paper, we investigate infer-and-widen approaches in three vignettes: the winner's curse, maximal contrasts, and inference after the lasso. In each of these examples, we show that a state-of-the-art infer-and-widen proposal leads to confidence intervals that are wider than a non-infer-and-widen alternative. Furthermore, even an ``oracle'' infer-and-widen confidence interval -- the narrowest possible interval that could be theoretically attained via infer-and-widen -- can be wider than the alternative.