Jakob Runge
@jakobrunge.bsky.social
๐ค 309
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Professor of AI in the Sciences at University of Potsdam // causalinferencelab.com
Here's a recent intro talk on the Tigramite python package for causal inference on time series data (also works on non-time series):
www.youtube.com/watch?v=DZbL...
Github:
github.com/jakobrunge/t...
Part of the great Online Causal Inference Seminar series:
sites.google.com/view/ocis/home
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Jakob Runge: Causal Inference on Time Series Data with the Tigramite Package
YouTube video by Online Causal Inference Seminar
https://www.youtube.com/watch?v=DZbLQ-WLrD0
2 months ago
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Save the date! ๐ Climate Informatics will convene researchers at the intersection of weather & climate science, statistics, and machine learning, April 27-30, 2026 on our beautiful UNIL/EPFL campus by Lake Geneva in Lausanne, Switzerland. More info:
climateinformatics.org
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Climate Informatics
An open community at the intersection of climate science and data science.
https://climateinformatics.org
3 months ago
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reposted by
Jakob Runge
Riccardo
9 months ago
๐ The first edition of the "Causal Abstractions and Representations" workshop is here! ๐ง๐ท Proudly hosted by
@auai.org
, we'll be in Rio de Janeiro on July 25, 2025. ๐จ Check out our invited speakers and the call for papers: we can't wait to see your submissions! ๐
sites.google.com/view/car-25/
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CAR
Causal Abstractions and RepresentationsWorkshop @ UAI 2025July 25th 2025, Rio de Janeiro ๐ง๐ท
https://sites.google.com/view/car-25/
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Got multivariate X and/or Y and want to test conditional independence X _|_ Y | Z ? PairwiseMultCI is a wrapper that turns any univariate CI test into a multivariate one... it can also help increase power! Tigramite tutorial
github.com/jakobrunge/t...
UAI Paper
proceedings.mlr.press/v216/hochspr...
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tigramite/tutorials/causal_discovery/tigramite_tutorial_pairwise_mult_ci.ipynb at master ยท jakobrunge/tigramite
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at - jakobrunge/tigramite
https://github.com/jakobrunge/tigramite/blob/master/tutorials/causal_discovery/tigramite_tutorial_pairwise_mult_ci.ipynb
about 1 year ago
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Causal discovery loves conditional independence tests -- here's our CI test of the month: ParCorrWLS can deal with heteroskedastic data! Tigramite tutorial:
github.com/jakobrunge/t...
NeurIPS paper:
proceedings.neurips.cc/paper_files/...
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tigramite/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb at master ยท jakobrunge/tigramite
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at - jakobrunge/tigramite
https://github.com/jakobrunge/tigramite/blob/master/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb
about 1 year ago
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Hello BlueSky! This is the start of my new timeline, gradually moving away from X/Twitter. Thanks for following! I will share new work here and look forward to exchanging ideas on causal inference for time series data with you!
about 1 year ago
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Bagged-PCMCI+ is out! A bootstrap approach to enhance precision+recall and confidence quantification for causal links. Check out our CLeaR paper:
https://rb.gy/6t5gba
and tigramite tutorial:
https://rb.gy/db9ro1
#CLeaR2024
@ELLISforEurope @ERC_Research @KDebeire
over 1 year ago
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Contribute to the 2nd workshop on causal inference for time series at UAI, this year on July 19 in Barcelona! Deadline for submissions is May 19, less than 10 days to go! Looking forward to another successful event! Details:
https://sites.google.com/view/ci4ts2024/home
#CI4TS
#UAI2024
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Causal inference for time series
About the workshop
https://sites.google.com/view/ci4ts2024/home
over 1 year ago
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Got causal questions and multiple datasets collected for different contexts/conditions/subjects/locations? Try J-PCMCI+, a causal discovery method for multiple time-series datasets, able to handle both observed and latent context-confounding!
https://proceedings.mlr.press/v216/gunther23a.html
almost 2 years ago
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How do you choose you pet friend in the zoo of causal inference methods? Our recent JMLR paper provides a comprehensive benchmark comparison for the bivariate cause-effect challenge:
https://www.jmlr.org/papers/v24/22-0151.html
. For this and many more benchmarks visit
http://causeme.net
!
almost 2 years ago
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A view-only version can be found here:
https://rdcu.be/dfs5X
over 2 years ago
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1/3 Just published @NatRevEarthEnv
https://tinyurl.com/3zb8cu7s
. A guide to
#causalinference
for time series: Phrase your problem as a causal Question, transparently state Assumptions, and apply the right method on your Data with the QAD-template based on @yudapearl's causal hierarchy
over 2 years ago
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Looking for a gentle introduction to
#causalinference
and relations to
#MachineLearning
learning? Kenneth Styppa heads a blog series of our group at
https://medium.com/causality-in-data-science
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Causality in Data Science โ Medium
In this blog researchers and practitioners from the causal inference research group at the german aerospace center publish easy to read blog articles that should give an introduction to the topics of causal inference in machine learning.
https://medium.com/causality-in-data-science
over 2 years ago
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We are hiring a scientific programmer (German E13 salary) in Jena to support tigramite and other software projects! Interested? More on
https://climateinformaticslab.com/jobs/
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Jobs
https://climateinformaticslab.com/jobs/
over 2 years ago
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To all (too)late-submitters: We extended the paper deadline by a few days, it is now June 03 11:59AM UTC-0 !
add a skeleton here at some point
over 2 years ago
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Few days left to submit to our UAI workshop on causal inference for time series data!
add a skeleton here at some point
over 2 years ago
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Happy to announce a workshop on causal inference for time series data at @UncertaintyInAI in August this year, together with @saramagliacane @ckassaad @JonasChoice and others! Including a Call4Papers ๐
https://sites.google.com/view/ci4ts2023
over 2 years ago
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Great to be attending @Climformatics in lovely Cambridge! Congratulations to the organisers for setting it up so well!
https://twitter.com/Climformatics/status/1648607027671834624
over 2 years ago
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Join the Causal Inference Lab! Open
#postdoc
position on developing
#causality
methods for a range of application domains! -->
http://climateinformaticslab.com
@DLRdatascience @ELLISforEurope @ERC_Research
about 3 years ago
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1/2: We just published a paper in @EnvDataScience where we present the SAVAR model, a spatiotemporal toy model for benchmarking causal inference methods. Congratulations to Xavi Tibau! And now time to test your causal methods!
https://bit.ly/3LQA7zh
over 3 years ago
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**Join the Causal Inference and Climate Informatics Lab!** We are expanding with 4 open
#postdoc
positions (#phd also possible) on developing
#causality
theory and methods for
#EarthSciences
and beyond! ๐
http://climateinformaticslab.com
@DLRdatascience @ELLISforEurope @ERC_Research
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News
http://climateinformaticslab.com
almost 4 years ago
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What are the causes behind recent extreme floods? Coming from math/stats/physics/ML and want to develop
#Causality
and
#AI
theory and methods to better understand extremes? --> Open positions with @ZscheischlerJak and further collaborators:
http://climateinformaticslab.com
@Compound_Event
over 4 years ago
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Got a background in math/stats/physics/ML and want to work on
#Causality
and
#AI
inspired by challenges in
#EarthSciences
and
#ClimateChange
? --> Well-funded
#Postdoc/#PhD
posts at @TUBerlin and @DLR_en Jena! -->
http://climateinformaticslab.com
@ELLISforEurope @ERC_Research
almost 5 years ago
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The ClimateInformaticsLab starts a new branch at @TUBerlin as part of my ERC Starting Grant
#CausalEarth
. We have two upcoming
#Postdoc/#PhD
positions in
#Berlin
on
#Causality
and
#AI
for
#Earth
sciences. More info:
https://climateinformaticslab.com
@ELLISforEurope @ERC_Research @EU_H2020
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News
https://climateinformaticslab.com
about 5 years ago
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Keep an eye on upcoming exciting PhD and postdoc opportunities within the ELLIS network that fosters AI in Europe. Soon I'll also announce some positions in my lab as part of my recent ERC Starting grant:
https://ellis.eu/de/news/ellis-phd-program-call-for-applications
over 5 years ago
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1/3 Checkout my new UAI paper on time series causal discovery for lagged AND contemporaneous dependencies (PCMCI+):
http://www.auai.org/uai2020/proceedings/579_main_paper.pdf
over 5 years ago
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Great joint work!!!
https://twitter.com/Marlene_Climate/status/1278638693205659649
over 5 years ago
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Looking for a PhD on developing machine learning / causal inference methods for climate research? Two open positions within @EU_H2020 training programme @iMIRACLI_ITN:
https://t.co/JKgHunBiQ2
and
https://t.co/5fNPQHWQVv
over 5 years ago
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Offering *two* PhD positions in @EU_H2020 training programme @iMIRACLI_ITN: Work with me on developing exciting causal inference methods for climate research.
https://t.co/JKgHunBiQ2
and
https://t.co/5fNPQHWQVv
over 5 years ago
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Just published in Nature Communications, check out our new causal inference approach to compare and evaluate climate models! Based on the causal discovery method in
https://advances.sciencemag.org/content/5/11/eaau4996
.
https://twitter.com/NatureComms/status/1239568643463294977
almost 6 years ago
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Just published: Climate Informatics 2019 proceedings (
https://t.co/M7gQ6FvONq
), and also invited speaker videos (
https://t.co/VYJb4xKZBU
). Thanks to all ~170 participants and see you in September (around 23-26) for Climate Informatics 2020 in Oxford!
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CI2019 - Proceedings
Brajard, J., Charantonis, A., Chen, C., & Runge, J. (Eds.). (2019). Proceedings of the 9th International Workshop on Climate Informatics: CI 2019 (No. NCAR/TN-561+PROC). doi:10.5065/y82j-f154
https://sites.google.com/view/climateinformatics2019/proceedings
almost 6 years ago
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I'm part of the @EU_H2020 project @iMIRACLI_ITN that offers *15 PhD studentships*. Work with me on developing new causal inference methods for climate research or on many other exciting topics, apply here:
http://www.imiracli.eu/
https://twitter.com/PhilipStier/status/1206602814908248065
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Home
http://www.imiracli.eu/
about 6 years ago
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https://twitter.com/runge_jakob/status/1206005853326893062?s=20
https://twitter.com/sweichwald/status/1206000943310999553
about 6 years ago
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Just presented the results of the Causality 4 Climate competition at
#NeurIPS2019
, watch the session here:
https://t.co/0z52bfknTz
(first hour) Congrats to the three winners Copenhagen Causality Lab, Mapefast, and Rookie! Many more benchmarks are on
https://t.co/GcPK82DS0I
!
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Causality for Climate (C4C)
https://slideslive.com/38922052/competition-track-day-21
about 6 years ago
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Here's a Perspective/Review paper for everyone interested in causal inference for Earth system sciences:
https://nature.com/articles/s41467-019-10105-3
(I know, it's already 6 months old, but hey, I just joined Twitter :-)
about 6 years ago
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Actually the skull picture is a nice example of confounding: It's from the Science Advances cover, not our paper;) Find code for our methods here:
https://github.com/jakobrunge/tigramite
https://twitter.com/yudapearl/status/1201294144888438786
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GitHub - jakobrunge/tigramite: Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at - jakobrunge/tigramite
https://github.com/jakobrunge/tigramite
about 6 years ago
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