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 Mondays, at 8:30am PT / 11:30am ET / 4:30pm London / 5:30pm Amsterdam / 6:30pm Tel Aviv. 

• Monday, October 5, 2026 [link]

  • Speaker: Jelle Goeman (Leiden University)

  • Title: A Uniform Improvement of the Benjamini-Hochberg Procedure via e-Closure

  • Abstract: This paper presents closed BH, a uniform improvement of the False Discovery Rate controlling method of Benjamini and Hochberg (BH). Closed BH is valid under the same assumption of Positive Regression Dependency on a Subset (PRDS) as BH, but also under an alternative and weaker minimal sufficient condition. As a uniform improvement, closed BH never rejects fewer hypotheses than BH, but it may reject quite a few more. An increase in power is observed especially when the number of false null hypotheses is large. The novel method is constructed using the e-Closure principle, a recently derived general principle for multiple testing. The method is implemented in the eClosure package in R.

  • Discussant: Aaditya Ramdas (Stanford University)

  • Links:  [Relevant papers: paper #1]

• Monday, October 12, 2026 [link]

  • Speaker: Anders Kock (University of Oxford)

• Monday, October 26, 2026 [link]

  • Speaker: Ruodu Wang (University of Waterloo)

• Monday, November 2, 2026 [link]

  • Speaker: Kevin Chen (Stanford University)

  • Title: Normal approximations in nonparametric empirical Bayes

  • Abstract: Empirical Bayes analyses routinely model noisy measurements of latent parameters as normal, justifying this by an informal appeal to the central limit theorem (CLT). This paper puts this heuristic appeal on firmer analytical grounds. We show that the denoising regret of the nonparametric maximum likelihood estimator (NPMLE) and related sieve methods is controlled by the rate attained under exact normality, plus a term reflecting the quality of the CLT approximation. The CLT need only hold marginally for each coordinate, and moreover only on average, without needing high-dimensional normal approximations. We identify two asymptotic regimes in which the normal approximation is adequate and the empirical Bayesian prior remains informative, and we show that our guarantees are robust to dependence and to variance estimation.

  • Discussant: 

  • Links:  [Relevant papers: paper #1]

Upcoming Seminar Presentations

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!