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

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!

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!

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

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

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.

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

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

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

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

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 :-)