In an era characterized by the exponential growth of data complexity and the increasing need for evidence-based decision-making, the field of causal inference [1] has emerged as a pivotal and rapidly evolving area of research. Methodological research in causal inference aims to equip domain scientists with tools to move beyond mere correlations and conduct data analyses that discern and quantify the effects of the true drivers of observed outcomes [2]. Such progress relies on rigorous causal models that facilitate inference about the effects of interventions, combined with meticulous statistical uncertainty assessment. The broader significance of causal discovery and inference lies in their transformative potential across multiple disciplines. By disentangling cause-and-effect relationships from mere associations, researchers can uncover the underlying mechanisms governing complex phenomena across domains such as biomedical discoveries, social sciences, and public policy [3]. In this way, causal analyses provide a solid foundation for evidence-based practices and interventions.
This Focus Topic serves as a platform for collaboration and knowledge exchange between researchers engaged in advancing the methodological and theoretical foundations of causal inference, and domain scientists at TUM. A particular focus lies on the domains of climate science [4], computational biology [5], and economics [6], which feature rich and complex data sources that constitute not only a fertile ground for application of techniques from causal inference but also prompt new methodological challenges [7].
[1] Pearl, Judea. Causality. Cambridge University Press, 2009. [2] Hernán, Miguel A., and James M. Robins. Causal inference. CRC Press, 2010. [3] Imbens, Guido W., and Donald B. Rubin. Causal inference in statistics, social, and biomedical Sciences. Cambridge University Press, 2015. [4] Runge, Jakob, et al. "Inferring causation from time series in Earth system sciences." Nature Communications 10.1 (2019): 2553. [5] Rivas-Barragan, Daniel, et al. "Drug2ways: Reasoning over causal paths in biol. networks for drug disc." PLoS Computational Biology 16.12 (2020). [6] Athey, Susan and Imbens, Guido W. “The state of applied econometrics: causality and policy eval.” Journal of Economic Perspectives 31.2 (2017): 3-32. [7] Schölkopf, Bernhard, et al. "Toward causal representation learning." Proceedings of the IEEE 109.5 (2021).
News
CAUSE Junior Researcher Day Spring 2024
CAUSE,
Event
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On April 11, the CAUSE Junior Researchers Day will take place, where members of research groups from various universities will give lectures on the topic of causality.
The CAUSE Team invites all of you as well as junior researchers from your groups interested in causality to come listen to the talks and meet other junior researchers in the field. Additionally, we invite anyone from your respective groups interested in showcasing their current research with a poster at the poster session to participate.
We will have invited talks from 14:00 - 17:00 given by junior members from selected research groups at other universities (CISPA Saarbrücken, DLR Jena, LMU Munich, ETH Zurich) and a poster session from 17:00 - 18:00. Afterwards, we invite everyone to join us for an informal dinner at a restaurant nearby.
We would greatly appreciate it if everyone planning to attend the event could complete the short registration form below by the beginning of next week (as well as the poster registration form for anyone planning to present a poster).