ArticleUncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence2017
Causal Discovery from Temporally Aggregated Time Series.
Article in Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Causal Learning through Deliberate Undersampling.Proceedings of machine learning research · 2023Article
- Review
- Inferring causation from time series in Earth system sciences.Nature communications · 2019Review
- Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.Proceedings of machine learning research · 2019Article
- Review of Causal Discovery Methods Based on Graphical Models.Frontiers in genetics · 2019Review
- Learning causality and causality-related learning: some recent progress.National science review · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Discovering causal structure of a dynamical system from observed time series is a traditional and important problem. In many practical applications, observed data are obtained by applying subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsampling refers to the procedure that for every
Identifiers
29899680PMC5995575What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.