Causal inference for time series

Runge, Jakob; Gerhardus, Andreas; Varando, Gherardo; Eyring, Veronika; Camps‐Valls, Gustau

doi:10.1038/s43017-023-00431-y

Cited by 47 publications

(28 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This will require that empirical data on disturbances are paired with environmental data, including from Earth Observation. Driving modern causal methods (e.g., causal network learning algorithms and structural causal model framework; Runge et al, 2023; Runge, Bathiany, et al, 2019; Runge, Nowack, et al, 2019), with this combination of data, could substantially advance the state of the art in understanding and quantifying complex dynamical systems of forest disturbance.…”

Section: Possible Applications Of the Defid2 Databasementioning

confidence: 99%

The Database of European Forest Insect and Disease Disturbances: DEFID2

Forzieri,

Dutrieux,

Elia

et al. 2023

Global Change Biology

View full text Add to dashboard Cite

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan‐European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate‐driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite‐based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral–temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large‐scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land‐climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc‐opendata/FOREST/DISTURBANCES/DEFID2/.

show abstract

Section: Possible Applications Of the Defid2 Databasementioning

confidence: 99%

The Database of European Forest Insect and Disease Disturbances: DEFID2

Forzieri,

Dutrieux,

Elia

et al. 2023

Global Change Biology

View full text Add to dashboard Cite

show abstract

“…After the three phases, under the standard assumptions of causal sufficiency, faithfulness, and the Markov condition as well as causal stationarity over the respective chosen time period (Runge et al, 2023), the outcome of the PCMCI+ algorithm is a causal graph with the four types of links: (a) directed lagged causal links for τ > 0, where τ stands for the time lag, (b) directed contemporaneous causal links for τ = 0, (c) unoriented contemporaneous links indicating that the collider and orientation rules could not be applied due to Markov equivalence, and (d) unoriented contemporaneous links where a direction is not defined due to conflicting orientation rules. An example of a causal graph is shown in Figure S1 in Supporting Information S1.…”

Section: Methodsmentioning

confidence: 99%

Causal Analysis Discovers an Enhanced Impact of Tropical Western Pacific on Indian Summer Monsoon Subseasonal Anomalies

Du,

Subramanian,

Han

et al. 2024

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Existing studies have shown changes in the impact of atmospheric teleconnections on Indian Summer Monsoon (ISM) at interannual time scales due to the changing background state. However, the exploration of potential changes at subseasonal time scales remains limited. In this study, we use a causal discovery method to find the tropical atmospheric drivers of ISM subseasonal anomalies, and quantify the causal effects of the drivers on ISM, as well as the changes in causal effects over the past 40 years. We find the impact of tropical western Pacific on ISM subseasonal anomalies has strengthened, while the ISM self‐feedback has weakened. Wavenumber‐frequency analysis shows that the Rossby wave energy over 15°N–30°N at periods of 10–20 days has increased over the past four decades, which offers a plausible explanation for the enhanced tropical atmospheric teleconnection between western Pacific and ISM at subseasonal time scales.

show abstract

“…Since all dynamic variables temporally exceed outcomes Y k , there are no edge from Y k to X in the graph model. Moreover, if we assume there are no unobserved confounders, then all backdoor paths from X to Y k is blocked by conditioning on variables excluding x i 55 , and the direct effect is identified as where is the reference value of x i , is the set of variables excluding x i ′. In Figure 6c and Supplements U , we specifically analyze the counterfactual prediction .…”

Section: Methodsmentioning

confidence: 99%

Ultra-efficient causal deep learning for Dynamic CSA-AKI Detection Using Minimal Variables

Zhong,

Cheng,

et al. 2023

Preprint

View full text Add to dashboard Cite

Cardiac surgery-associated Acute Kidney Injury (CSA-AKI) is a significant complication that often leads to increased morbidity and mortality. Effective CSA-AKI management relies on timely diagnosis and interventions. However, many cases of CSA-AKI are detected too late. Despite the efforts of novel biomarkers and data-driven predictive models, their limited discriminative and generalization capabilities along with stringent application requirements pose challenges for clinical use. Here we incorporate a causal deep learning approach that combines the universal approximation abilities of neural networks with causal discovery to develop REACT, a reliable and generalizable model to predict a patient’s risk of developing CSA-AKI within the next 48 hours. REACT was developed using 21.5 billion time-stamped medical records from two large hospitals covering 23,933 patients and validated in three independent centers covering 30,963 patients. By analyzing the causal relationships buried in the time dimensions, REACT distilled the complex temporal dynamics among variables into six minimal causal inputs and achieved an average AUROC of 0.93 (ranging from 0.89 to 0.96 among different CSA-AKI stages), surpassing state-of-the-art models that depend on more complex variables. This approach accurately predicted 97% of CSA-AKI events within 48 hours for all prediction windows, maintaining a ratio of 2 false alerts for every true alert, improving practical feasibility. Compared to guideline-recommended pathways, REACT detected CSA-AKI on average 16.35 hours earlier in external tests. In addition, we have established a publicly accessible website and performed prospective validation on 754 patients across two centers, achieving high accuracy. Our study holds substantial promise in enhancing early detection and preserving critical intervention windows for clinicians.

show abstract

Causal inference for time series

Cited by 47 publications

References 97 publications

The Database of European Forest Insect and Disease Disturbances: DEFID2

The Database of European Forest Insect and Disease Disturbances: DEFID2

Causal Analysis Discovers an Enhanced Impact of Tropical Western Pacific on Indian Summer Monsoon Subseasonal Anomalies

Ultra-efficient causal deep learning for Dynamic CSA-AKI Detection Using Minimal Variables

Contact Info

Product

Resources

About