by Jakob Runge
Save the date! 🌍
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
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
Climate Informatics
An open community at the intersection of climate science and data science.
climateinformatics.org
Reposted by Jakob Runge
🎉 The first edition of the "Causal Abstractions and Representations" workshop is here!
🇧🇷 Proudly hosted by @auai.org, we'll be in Rio de Janeiro on July 25, 2025.
🔨 Check out our invited speakers and the call for papers: we can't wait to see your submissions!
🌐 sites.google.com/view/car-25/
🇧🇷 Proudly hosted by @auai.org, we'll be in Rio de Janeiro on July 25, 2025.
🔨 Check out our invited speakers and the call for papers: we can't wait to see your submissions!
🌐 sites.google.com/view/car-25/
CAR
Causal Abstractions and Representations Workshop @ UAI 2025 July 25th 2025, Rio de Janeiro 🇧🇷
sites.google.com
by Jakob Runge
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...
Tigramite tutorial github.com/jakobrunge/t... UAI Paper proceedings.mlr.press/v216/hochspr...
tigramite/tutorials/causal_discovery/tigramite_tutorial_pairwise_mult_ci.ipynb at master · jakobrunge/tigramite
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at - jakobrunge/tigramite
github.com
by Jakob Runge
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/...
tigramite/tutorials/causal_discovery/tigramite_tutorial_heteroskedastic_ParCorrWLS.ipynb at master · jakobrunge/tigramite
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at - jakobrunge/tigramite
github.com
by Jakob Runge
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!
by Jakob Runge
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
by Jakob Runge
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
Causal inference for time series
About the workshop
sites.google.com
by Jakob Runge
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
by Jakob Runge
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!
by Jakob Runge
A view-only version can be found here: https://rdcu.be/dfs5X
by Jakob Runge
3/3 Another highlight: A decision diagram to find the right approach for your problem. Great joint work with Andreas Gerhardus, Gherardo Varando, Veronika Eyring, and Gustau Camps-Valls. Integrate causal thinking into your #MachineLearning data-driven science!
by Jakob Runge
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
by Jakob Runge
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
Causality in Data Science – 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.
medium.com
by Jakob Runge
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/
Jobs
climateinformaticslab.com
by Jakob Runge
To all (too)late-submitters: We extended the paper deadline by a few days, it is now June 03 11:59AM UTC-0 !
by Jakob Runge
Few days left to submit to our UAI workshop on causal inference for time series data!
by Jakob Runge
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 👉 https://sites.google.com/view/ci4ts2023
by Jakob Runge
Great to be attending @Climformatics in lovely Cambridge! Congratulations to the organisers for setting it up so well! https://twitter.com/Climformatics/status/1648607027671834624
by Jakob Runge
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
by Jakob Runge
2/2 The paper also introduces Mapped-PCMCI, a spatio-temporal causal discovery method to reconstruct causal networks from gridded data: Going from correlation to causal networks utilizing the assumption of a lower-dimensional latent causal process.
by Jakob Runge
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
by Jakob Runge
PS: We now have *flexible home office rules* at DLR enabling you to partially also work from another city than Jena.
by Jakob Runge
**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!
👉 http://climateinformaticslab.com
@DLRdatascience @ELLISforEurope @ERC_Research
We are expanding with 4 open #postdoc positions (#phd also possible) on developing #causality theory and methods for #EarthSciences and beyond!
👉 http://climateinformaticslab.com
@DLRdatascience @ELLISforEurope @ERC_Research
News
climateinformaticslab.com
by Jakob Runge
2/2 Interested to develop #Causality and #AI theory and methods and work with climate scientists to better understand climate change and extremes? Join our team at http://climateinformaticslab.com
News
climateinformaticslab.com
by Jakob Runge
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
by Jakob Runge
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
by Jakob Runge
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
News
climateinformaticslab.com
by Jakob Runge
3/3 LPCMCI is based on the flexible constraint-based causal discovery framework and can deal with linear and nonlinear, lagged and contemporaneous causal relationships. Now part of https://github.com/jakobrunge/tigramite
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
github.com
by Jakob Runge
2/3 LPCMCI extends PCMCI+ (http://www.auai.org/uai2020/proceedings/579_main_paper.pdf) to the latent confounder case and shows strong gains in recall compared to previous methods for the ubiquitous high-autocorrelation case.
by Jakob Runge
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