Jakob Zeitler

PhD at the UCL Centre for Artificial Intelligence


Big maths and I.

I research methods and limitations of causal inference and their intersection with machine learning.

I believe that causal inference can work in the real world only if we are honest about its assumptions. That is why I am looking at problem settings with trustworthy properties:

Message me at mail@firstname-lastname.de


Jan 12, 2023 Two papers accepted at CLeaR (Causal Learning and Reasoning) 2023
Non-parametric identifiability and sensitivity analysis of synthetic control models (with Spotify)
Stochastic Causal Programming for Bounding Treatment Effects
Oct 22, 2022 New page, new life. Moving over from https://jakobzeitler.weebly.com
Oct 19, 2022 Presented our recent work on partial identification at the Institute for Data Science and Artificial Intelligence. Recording here.
Sep 19, 2022 Completed my summer intership at Spotify’s new Advanced Causal Inference lab. Together with Ciaran Lee I investigated the assumptions of synthetic control, hoping to share results on that beginning 2023.
Oct 20, 2019 Started my PhD with Ricardo Silva at the new Centre for Doctoral Training in Foundational Artificial Intelligence. Thanks to UKRI and DeepMind for the generous funding.

selected publications

  1. The Causal Marginal Polytope for Bounding Treatment Effects
    Jakob Zeitler, and Ricardo Silva
    arXiv preprint arXiv:2202.13851 2022
  2. Stochastic Causal Programming for Bounding Treatment Effects
    Kirtan Padh, Jakob Zeitler, David Watson, and 3 more authors
    arXiv preprint arXiv:2202.10806 2022
  3. Algorithmic recourse in partially and fully confounded settings through bounding counterfactual effects
    Julius Kügelgen, Nikita Agarwal, Jakob Zeitler, and 2 more authors
    arXiv preprint arXiv:2106.11849 2021