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

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Upcoming Seminar Presentations

All seminars take place Thursdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 6:30 pm Tel Aviv. Past seminar presentations are posted here.


  • Thursday, August 12, 2021 [Link to join]

    • Speaker: Sanat K. Sarkar (Temple University)

    • Title: Adjusting the Benjamini-Hochberg method for controlling the false discovery rate in knockoff-assisted variable selection

    • Abstract: The knockoff-based multiple testing setup of Barber & Candès (2015) for variable selection in multiple regression where sample size is as large as the number of explanatory variables is considered. The Benjamini-Hochberg method based on ordinary least squares estimates of the regression coefficients is adjusted to the setup, transforming it to a valid p-value based FDR controlling method not relying on any specific correlation structure of the explanatory variables. Simulations and real data applications show that our proposed method that is agnostic to $\pi_0$, the proportion of unimportant explanatory variables, and a data-adaptive version of it that uses an estimate of $\pi_0$ are powerful competitors of the FDR controlling methods in Barber & Candès (2015).

    • Discussant: Lucas Janson (Harvard University)

    • Links: [Relevant papers: paper #1]

Format

The seminars are held on Zoom and last 60 minutes:

  • 45 minutes of presentation

  • 15 minutes of discussion, led by an invited discussant

Moderators collect questions using the Q&A feature during the seminar.

How to join

You can attend by clicking the link to join (there is no need to register in advance).

More instructions for attendees can be found here.

Organizers

Contact us

If you have feedback or suggestions or want to propose a speaker, please e-mail us at selectiveinferenceseminar@gmail.com.

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!