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9 results for “Causal Events”
Wikidata Causal Event Triple Data
<p>This dataset contains triples curated from Wikidata surrounding news events with causal relations, and is released as part of our WWW'23 paper, "Event Prediction using Case-Based Reasoning over Knowledge Graphs".</p> <p>Starting from a set of classes that we consider to be types of "events", we queried Wikidata to collect entities that were an instanceOf an event class and that were connected to another such event entity by a causal triple (https://www.wikidata.org/wiki/Wikidata:List_of_properties/causality). For all such cause-effect event pairs, we then collected a 3-hop neighborhood of outgoing triples.</p>
CLEVRER-Humans: Describing physical and causal events the human way
<p>Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of causal relationships. This design brings up two issues. First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels. We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models. We convert the collected CEGs into questions and answers to be consistent with prior work. Finally, we study a collection of baseline approaches for CLEVRER-Humans question-answering, highlighting the great challenges set forth by our benchmark.</p>
CLEVRER-Humans: Describing physical and causal events the human way
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Past Causalities and Event Categories for Connecting Similar Past and Present Causalities
<p>This dataset includes past causalities and their categories to connect similar past and present causalities. We report how to use this dataset in the following papers.</p> <p><em>Ryohei Ikejiri, Yasunobu Sumikawa: "Developing world history lessons to foster authentic social participation by searching for historical causation in relation to current issues dominating the news". Journal of Educational Research on Social Studies 84, 37–48 (2016). (in Japanese).</em></p> <p><em>Yasunobu Sumikawa and Ryohei Ikejiri, "Mining Historical Social Issues", Intelligent Decision Technologies, Smart Innovation, IDT'15, Systems and Technologies, Vol. 39, Springer, pp. 587--597, 2015.</em></p> <p>This dataset is based on some textbooks that are popular ones in Japanese high-school. We first collect past causalities by referencing the textbooks. We then select the causalities if they can be useful for considering solutions for present social issues. To enhance the analogy, we describe each causality in three kinds of texts: background including problems, solution ways, and their results. From the selected causalities and an Encyclopedia of Historiography, we define categories for them. Finally, the created dataset contains 138 past causalities and 13 categories. Each past causality has more than one categories.</p> <p>To help training machine learning models, this dataset additionally provides 900 past event data in past_events_wikipedia.tsv. The event data were collected from Wikipedia, and then were assigned one or more categories from the above 13 ones. We have confirmed that SVM-RBF equipped with the above all categorized data obtained 73.6% precision, 55.8% recall and 63.5% F1 score</p> <p> </p> <p><strong>File contents</strong>:</p> <ul> <li>Past causality data <ol> <li>historical_causalities_data.tsv: Detail of stored causalities.</li> <li>historical_causalities_regions.tsv: Regions where the causalities happened.</li> <li>historical_causalities_categories.tsv: Categories of the causalities.</li> </ol> </li> <li>Past event data <ol> <li>past_events_wikipedia.tsv: Descriptions of past events stored in Wikipedia. This file is useful for training machine learning model such as SVM.</li> </ol> </li> <li>Statistics (Statistics.tsv) <p> Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+JS divergence, and Meta-data Similarity that counts how many common categories two causalities share in order to measure qualities of the dataset.</p> </li> </ul> <p><strong>Grants</strong>: JSPS KAKENHI Grant Number 26750076, 17K12792, and 19K20631</p>
Data from: Methods for estimating causal relationships of adverse events with dietary supplements
Objective: Dietary supplement use has increased over past decades, resulting in reports of potentially serious adverse events. The aim of this study was to develop optimised methods to evaluate the causal relationships between adverse events and dietary supplements, and to test these methods using case reports. Design: Causal relationship assessment using prospectively collected data. Setting and participants: 4 dietary supplement experts, 4 pharmacists and 11 registered dietitians (5 men and 14 women) examined 200 case reports of suspected adverse events using the modified Naranjo scale and the modified Food and Drug Administration (FDA) algorithm. Primary outcome measures: The distribution of evaluation results was analysed and inter-rater reliability was evaluated for the two modified methods employed using intraclass correlation coefficients (ICC) and Fleiss' κ. Results: Using these two methods, most of the 200 case reports were categorised as 'lack of information' or 'possible' adverse events. Inter-rater reliability among entire assessors ratings for the two modified methods, based on ICC and Fleiss' κ, were classified as more than substantial (modified Naranjo scale: ICC (95% CI) 0.873 (0.850 to 0.895); Fleiss' κ (95% CI) 0.615 (0.615 to 0.615). Modified FDA algorithm: Fleiss' κ (95% CI) 0.622 (0.622 to 0.622). Conclusions: These methods may help to assess the causal relationships between adverse events and dietary supplements. By conducting additional studies of these methods in different populations, researchers can expand the possibilities for the application of our methods.
Data from: Methods for estimating causal relationships of adverse events with dietary supplements
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Comprehensive investigation of circulating biomarkers and their causal role in atherosclerosis-related risk factors and clinical events
<p><strong>Background</strong>: Circulating biomarkers have been previously associated with atherosclerosis-related risk factors, but the nature of these associations is incompletely understood.</p> <p><strong>Methods</strong>: We performed multivariable-adjusted regressions and 2-sample Mendelian randomization analyses to assess observational and causal associations of 27 circulating biomarkers with 7 cardiovascular traits in up to 451 933 participants of the UK Biobank.</p> <p><strong>Results</strong>: After multiple-testing correction (alpha=1.3Å~10−4), we found a total of 15, 9, 21, 22, 26, 24, and 26 biomarkers strongly associated with coronary artery disease, ischemic stroke, atrial fibrillation, type 2 diabetes, systolic blood pressure, body mass index, and waist-to-hip ratio; respectively. The Mendelian randomization analyses confirmed strong evidence of previously suggested causal associations for several glucose- and lipid-related biomarkers with type 2 diabetes and coronary artery disease. Particularly interesting findings included a protective role of IGF-1 (insulin-like growth factor 1) in systolic blood pressure, and the strong causal association of lipoprotein(a) in coronary artery disease development (β, −0.13; per SD change in exposure and outcome and odds ratio, 1.28; P=2.6Å~10−4 and P=7.4Å~10−35, respectively). In addition, our results indicated a causal role of increased ALT (alanine aminotransferase) in the development of type 2 diabetes and hypertension (odds ratio, 1.59 and β, 0.06, per SD change in exposure and outcome; P=4.8Å~10−11 and P=6.0Å~10−5). Our results suggest that it is unlikely that CRP (C-reactive protein) and vitamin D play causal roles of any meaningful magnitude in development of cardiometabolic disease.</p> <p><strong>Conclusions</strong>: We confirmed and extended known associations and reported several novel causal associations providing important insights about the cause of these diseases, which can help accelerate new prevention strategies.</p>
Present Events and Event Categories for Connecting Similar Past and Present Causalities
<p>This dataset includes present events described as news articles and their categories to connect similar past and present causalities. </p> <p>There are 2 files: 13category_crawled_news.csv and 13category_mainichi_news_events.csv. Both the files include an ID for a present event and its categories per line. The former one uses IDs defined in CD-Mainichi Newspapers 2012 data whereas the latter one uses URLs.</p> <p> </p> <p>In these files, 0 represents that its category is not assigned to the event. In contrast, 1 is used to represent that the category is assigned to the event. For example, if a cell (<em>k</em>, <em>l</em>) is 0, the <em>k</em>th event is not related to the <em>l</em>th category.</p>
Comprehensive investigation of circulating biomarkers and their causal role in atherosclerosis-related risk factors and clinical events
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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