We’re looking for a managing director for the AI.UP center at the University of Potsdam. AI.UP develops and consults on AI across disciplines and supports interdisciplinary collaboration and grant initiatives. *Permanent position* Deadline: June 28, 2026 Details: causalinferencelab.com/jobs

We’re hiring 2 AI Research Consultants (Junior/Senior) at the University of Potsdam (AI.UP). TV-L E13 (100%), 4 years. Focus: interdisciplinary AI consulting and independent research. Deadline: March 22, 2026 Details: causalinferencelab.com/jobs

Hiring! Postdoc in Causal Inference & Explainable AI at University of Potsdam (CRC 1294 “Data Assimilation”). TV-L E13 (100%), until 06/2029. Start summer 2026. Deadline: March 22, 2026. Apply (ID 308/2026): [email protected] Details: causalinferencelab.com/jobs

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

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

🎉 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/

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...

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/...

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!

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

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

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

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!

A view-only version can be found here: https://rdcu.be/dfs5X

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

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

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/

To all (too)late-submitters: We extended the paper deadline by a few days, it is now June 03 11:59AM UTC-0 !

Few days left to submit to our UAI workshop on causal inference for time series data!

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

Great to be attending @Climformatics in lovely Cambridge! Congratulations to the organisers for setting it up so well! https://twitter.com/Climformatics/status/1648607027671834624

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

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

**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

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

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

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

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

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

Great joint work!!! https://twitter.com/Marlene_Climate/status/1278638693205659649

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

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

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

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!

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

https://twitter.com/runge_jakob/status/1206005853326893062?s=20 https://twitter.com/sweichwald/status/1206000943310999553

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 !

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 :-)

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