Jonas Peters
Full Professor
Researches causal inference and statistical learning, including invariant causal prediction and causal discovery from observational and interventional data.
Jonas Peters's lab at ETH Zurich works on probabilistic & causal inference, learning theory & optimization and efficient training & inference. 19 papers since 2023, 8 at top venues, mostly NeurIPS and ICML. 4 PhD students.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 42%
- PhD students
- 4
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 5 · collecting
- 3 papers2025 · T1 2
- 4 papers2024 · T1 1
- 2 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, Nature Machine Intelligence
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference7 papers
- causal7 papers
- Learning theory & optimization1 paper
- Efficient training & inference1 paper
- causal discovery0 papers
Paper list
From the lab homepage.
202610 papers
- NeurIPS (T1)Anchor PCA (opens in a new tab)
- ICML (T1)Anti-causal domain generalization: Leveraging unlabeled data (opens in a new tab)
- Journal of Machine Learning Research 27(46), 1--57Boosted Control Functions (opens in a new tab)
- Journal of the Royal Statistical Society, Series B 88(3), 799--818DecoR: Deconfounding Time Series with Robust Regression (opens in a new tab)
- NeurIPS (T1)FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes (opens in a new tab)
- Hydrology and Earth System Sciences 30(14), 4667--4698Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models (opens in a new tab)
- ICML (T1)Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible? (opens in a new tab)
- NeurIPS (T1)Prediction-Intervention Games and Invariant Sets (opens in a new tab)
- Water Resources Research 62(2)RESIdual STability (RESIST) calibration for improved hydrological model time generalizability (opens in a new tab)
- Journal of Causal Inference 14(1), 20250013Unfair Utilities and First Steps Towards Improving Them (opens in a new tab)
20253 papers
- Journal of Machine Learning Research 26(95), 1--56. .Invariant Subspace Decomposition (opens in a new tab)
- Nature Machine Intelligence (T1)The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology (opens in a new tab)
- NeurIPS (T1)Transferring Causal Effects using Proxies
20244 papers
- Journal of Machine Learning Research 25(34), 1--36, .Effect-Invariant Mechanisms for Policy Generalization (opens in a new tab)
- Journal of Machine Learning Research 25(302), 1--51, .Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past (opens in a new tab)
- ICLR (T1)Identifying Representations for Intervention Extrapolation (opens in a new tab)
- Journal of the American Statistical Association 120(550), 1090--1101, .Model-based causal feature selection for general response types (opens in a new tab)
20232 papers
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 54(7), .Invariant Policy Learning: A Causal Perspective (opens in a new tab)
- Journal of the Royal Statistical Society, Series B 85(3), 597--663, .Statistical Testing under Distributional Shifts (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ETH ZurichRolling
Not announced yet
Computer Science Doctorate (D-INFK)
- Round
- Rolling (no deadline)
- Opens
- —
- GRE
- See program page
- English
- Proficiency in written and spoken English
- Fee
- See program page
Confirmed on the program page.
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