期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-019-10105-3
关键词
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资金
- Netherlands Earth System Science Centre (NESSC)
- German Federal Ministry of Education and Research (BMBF) [01LN1304A]
- Netherlands Organisation for Scientific Research (NWO) [016.Vidi.171011]
- James S. McDonnell foundation
- European Research Council (ERC) [647423]
- Simons Foundation [318812]
- U.S. Office of Naval Research [N00014-15-1-2093]
- U.S. Army Research Office [W911NF-16-1-0081]
- European Union Horizon 2020 project BACI [640176]
- DoD-Strategic Environmental Research and Development Program [15 RC-2509]
- Lenfest Ocean Program [00028335]
- National Science Foundation [NSFDEB-1655203, NSF-ABI-Innovation DBI-1667584]
- United States Air Force [FA8650-17-C-7715]
- National Science Foundation EAGER Grant [IIS-1829681]
- National Institutes of Health [NIH-1R01EB022858-01, FAINR01EB022858, NIH-1R01LM012087, NIH5U54HG008540-02, FAIN-U54HG008540]
- Swiss National Science Foundation Ambizione grant [PZ00P2-179876]
- VILLUM FONDEN [18968]
- Swiss National Science Foundation (SNF) [PZ00P2_179876] Funding Source: Swiss National Science Foundation (SNF)
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme. net to close the gap between method users and developers.
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