International Seminar on Selective Inference

A weekly online seminar on selective inference, multiple testing, and post-selection inference.

Gratefully inspired by the Online Causal Inference Seminar

Mailing List

For announcements and Zoom invitations please subscribe to our mailing list. Our seminar (typically) runs on Thursdays, at 8:30am PT / 11:30am ET / 4:30pm London / 5:30pm Amsterdam / 6:30pm Tel Aviv.

Upcoming Seminar Presentations

• Thursday, February 5, 2026 [Link to join]

  • Speaker: Etienne Roquain (Sorbonne Université)

  • Title: Online selective conformal inference: adaptive scores, convergence rate and optimality

  • Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of \cite{gibbs2021adaptive}. For any given point-prediction algorithm, their method (ACI) produces a conformal prediction set with an average missed coverage getting close to a pre-specified level α for a long time horizon. We introduce an extended version of this algorithm, called OnlineSCI, allowing the user to additionally select times where such an inference should be made. OnlineSCI encompasses several prominent online selective tasks, such as building prediction intervals for extreme outcomes, classification with abstention, and online testing. While OnlineSCI controls the average missed coverage on the selected in an adversarial setting, our theoretical results also show that it controls the instantaneous error rate (IER) at the selected times, up to a non-asymptotical remainder term. Importantly, our theory covers the case where OnlineSCI updates the point-prediction algorithm at each time step, a property which we refer to as {\it adaptive} capability. We show that the adaptive versions of OnlineSCI can convergence to an optimal solution and provide an explicit convergence rate in each of the aforementioned application cases, under specific mild conditions. Finally, the favorable behavior of OnlineSCI in practice is illustrated by numerical experiments.

  • Discussant: Ying Jin (University of Pennsylvania)

  • Links:  [Relevant papers: paper #1]

• Thursday, February 12, 2026 [Link to join]

  • Speaker: Fei Xue (Purdue University)

  • Title: High-dimensional statistical inference for linkage disequilibrium score regression and its cross-ancestry extensions

  • Abstract: Linkage disequilibrium score regression (LDSC) has emerged as an essential tool for genetic and genomic analyses of complex traits, utilizing high- dimensional data derived from genome-wide association studies (GWAS). LDSC computes the linkage disequilibrium (LD) scores using an external reference panel, and integrates the LD scores with only summary data from the original GWAS. In this paper, we investigate LDSC within a fixed-effect data integration framework, underscoring its ability to merge multi-source GWAS data and reference panels. In particular, we take account of the genome-wide dependence among the high-dimensional GWAS summary statistics, along with the block-diagonal dependence pattern in estimated LD scores. Our analysis uncovers several key factors of both the original GWAS and reference panel datasets that determine the performance of LDSC. We show that it is relatively feasible for LDSC-based estimators to achieve asymptotic normality when applied to genome-wide genetic variants (e.g., in genetic variance and covariance estimation), whereas it becomes considerably challenging when we focus on a much smaller subset of genetic variants (e.g., in partitioned heritability analysis). Moreover, by modeling the disparities in LD patterns across different populations, we show that LDSC can be expanded to conduct cross-ancestry analyses using data from genetically distinct global populations. We validate our theoretical findings through extensive numerical evaluations using real genetic data from the UK Biobank study.

  • Discussant: Rajarshi Mukherjee (Harvard University)

  • Links:  [Relevant papers: paper #1]

• Thursday, February 19, 2026 [Link to join]

  • Speaker: Adam Jaffe (Columbia University) 

  • Title: Constrained Denoising, Empirical Bayes, and Optimal Transport

  • Abstract: In the statistical problem of denoising, Bayes and empirical Bayes methods can "overshrink" their output relative to the latent variables of interest. This work is focused on constrained denoising problems which mitigate such phenomena. At the oracle level, i.e., when the latent variable distribution is assumed known, we apply tools from the theory of optimal transport to characterize the solution to (i) variance-constrained, (ii) distribution-constrained, and (iii) general-constrained denoising problems. At the empirical level, i.e., when the latent variable distribution is not known, we use empirical Bayes methodology to estimate these oracle denoisers. Our approach is modular, and transforms any suitable (unconstrained) empirical Bayes denoiser into a constrained empirical Bayes denoiser. We prove explicit rates of convergence for our proposed methodologies, which both extend and sharpen existing asymptotic results that have previously considered only variance constraints. We apply our methodology in two applications: one in astronomy concerning the relative chemical abundances in a large catalog of red-clump stars, and one in baseball concerning minor- and major league batting skill for rookie players.

  • Discussant: Jake Soloff (University of Michigan)

  • Links:  [Relevant papers: paper #1]

• Thursday, March 5, 2026 [Link to join]

  • Speaker: Yanjun Han (New York University)

• Thursday, March 12, 2026 [Link to join]

  • Speaker: Peter Hoff (Duke University)

• Thursday, March 19, 2026 [Link to join]

  • Speaker: Yuval Benjamini (Hebrew University)

What is selective inference?

Broadly construed, selective inference means searching for interesting patterns in data, usually with inferential guarantees that account for the search process. It encompasses:

• Multiple testing: testing many hypotheses at once (and paying disproportionate attention to rejections)

• Post-selection inference: examining the data to decide what question to ask, or what model to use, then carrying out one or more appropriate inferences

• Adaptive / interactive inference: sequentially asking one question after another of the same data set, where each question is informed by the answers to preceding questions

• Cheating: cherry-picking, double dipping, data snooping, data dredging, p-hacking, HARKing, and other low-down dirty rotten tricks; basically any of the above, but done wrong!