Article
Infectious Diseases
Merce Espona, Daniel Echeverria-Esnal, Sergi Hernandez, Alexander Almendral, Silvia Gomez-Zorrilla, Enric Limon, Olivia Ferrandez, Santiago Grau
Summary: The introduction of generic antimicrobials may lead to an increase in consumption of broad-spectrum molecules and a potential rise in resistance, which could dilute the economic benefits of generic antibiotics. Antimicrobial stewardship should continue to monitor these molecules despite generic entry.
Article
Computer Science, Artificial Intelligence
Antonio Anastasio Bruto da Costa, Pallab Dasgupta
Summary: The study aims to extract temporal causal sequences to explain key events in time-series traces, with applications in design debugging, anomaly detection, planning, and root-cause analysis. It utilizes decision trees and interval arithmetic to mine sequences and proposes modified decision tree construction metrics to address the non-determinism introduced by the temporal dimension. The mined sequences are presented in a readable temporal logic language for easy interpretation, illustrated through various examples.
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
(2021)
Article
Multidisciplinary Sciences
Manuel Castro, Pedro Ribeiro Mendes Junior, Aurea Soriano-Vargas, Rafael de Oliveira Werneck, Maiara Moreira Goncalves, Leopoldo Lusquino Filho, Renato Moura, Marcelo Zampieri, Oscar Linares, Vitor Ferreira, Alexandre Ferreira, Alessandra Davolio, Denis Schiozer, Anderson Rocha
Summary: In this study, we propose using ensemble models (such as Random Forest) to assess the importance of input features in machine learning models, in order to establish causal relationships between variables. By analyzing oil field production data, we find that our results align with confirmed tracer information, demonstrating the effectiveness of our proposed methodology.
SCIENTIFIC REPORTS
(2023)
Article
Infectious Diseases
Melisa Barrantes-Gonzalez, Santiago Grau, David Conde-Estevez
Summary: This study aims to analyze the trends in tigecycline prescription after safety warnings by FDA and EMA, as well as after the implementation of an ASP. The results show that tigecycline consumption decreased by 35.9% after the FDA warning and by 67.3% after ASP implementation. This indicates that ASP plays a crucial role in controlling the prescription of tigecycline.
JOURNAL OF ANTIMICROBIAL CHEMOTHERAPY
(2022)
Article
Geography
Wenjia Zhang, Kexin Ning
Summary: This study explores the causal effects of mobility control policies during the early stages of the COVID-19 outbreak in Shenzhen, China, based on a five-month mobile phone big data set. The results reveal significant spatiotemporal heterogeneities in the policies' effects, with an abrupt decrease in travel distance after the implementation of public health emergency response and closed-off management of residential communities. These findings highlight the importance of incorporating spatiotemporal variations for fine-grained policy assessments.
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
(2023)
Article
Ecology
Suchinta Arif, M. Aaron MacNeil
Summary: Recent developments in computer science have advanced the use of causal inference and causal diagrams in observational studies, providing a unified approach to variable selection across different methodologies. This paper demonstrates how causal diagrams can be extended to ensure proper study design under quasi-experimental settings and highlights the importance of routinely applying causal diagrams in ecology research.
Article
Computer Science, Artificial Intelligence
Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, Huan Liu
Summary: This paper focuses on the application of time series data in causal inference, specifically on treatment effect estimation and causal discovery tasks. A comprehensive review of methods, evaluation metrics, and datasets is provided to serve as benchmarks for research in this field.
KNOWLEDGE AND INFORMATION SYSTEMS
(2021)
Article
Economics
Silvia Goncalves, Ulrich Hounyo, Andrew J. Patton, Kevin Sheppard
Summary: This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data. The authors show the consistency of the bootstrap distribution and bootstrap variance estimators, justifying the use of bootstrap percentile intervals and bootstrap standard errors.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
Review
Biology
Alex Eric Yuan, Wenying Shou
Summary: This article provides a critical review of three statistical causal discovery methods and their applications in ecological processes. The review examines what each method tests for, the causal statements it implies, and the potential for misinterpretation. The authors introduce new visualization techniques and highlight the limitations of so-called "model-free" causality tests. The goal of the review is to encourage thoughtful application of these methods, facilitate interdisciplinary communication, and promote explicit assumptions.
Review
Environmental Sciences
Jakob Runge, Andreas Gerhardus, Gherardo Varando, Veronika Eyring, Gustau Camps-Valls
Summary: Many research questions in Earth and environmental sciences require causal inference to establish relationships between variables. However, there is a language gap between methodological and domain science communities. This Technical Review explains the use of causal inference in time series data and provides practical case studies to address challenges and improve data-driven science in Earth and environmental sciences.
NATURE REVIEWS EARTH & ENVIRONMENT
(2023)
Article
Immunology
Kamila Romanowski, Michael R. Law, Mohammad Ehsanul Karim, Jonathon R. Campbell, Md Belal Hossain, Mark Gilbert, Victoria J. Cook, James C. Johnston
Summary: A study found that people treated for respiratory tuberculosis experience increased healthcare utilization in the years following treatment, with respiratory morbidity being a significant contributor. This highlights the importance of screening, assessment, and treatment for post-tuberculosis sequelae.
CLINICAL INFECTIOUS DISEASES
(2023)
Article
Computer Science, Theory & Methods
Masud Rana, Justin Kosar, Shaqil Peermohamed
Summary: Interrupted time series (ITS) design is a quasi-experimental approach used in public health research for evaluating the impact of an intervention. However, the use of aggregated data can lead to issues, so we proposed three models to address different data limitations. By comparing the performance of different models, we found that hierarchical models have good applicability and performance.
STATISTICS AND COMPUTING
(2023)
Article
Computer Science, Software Engineering
Zikun Deng, Di Weng, Xiao Xie, Jie Bao, Yu Zheng, Mingliang Xu, Wei Chen, Yingcai Wu
Summary: This paper presents Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series. Compass addresses the challenges of detecting urban causality, interpreting dynamic causal relations, and unveiling suspicious causal relations. It provides an effective tool for analyzing urban phenomena using causal graphs and visualizations.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Multidisciplinary Sciences
Zhipeng Ma, Marco Kemmerling, Daniel Buschmann, Chrismarie Enslin, Daniel Luetticke, Robert H. Schmitt
Summary: Causal inference is a fundamental research topic for discovering cause-effect relationships. Time series data provides a good basis for inferring causal relationships. This publication proposes a new data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms.
Article
Computer Science, Interdisciplinary Applications
Lipeng Cui, Jack Murdoch Moore
Summary: This paper systematically benchmarks and contrasts six widely used methods for causal network reconstruction, finding that convergent cross mapping consistently provides the highest precision, while transfer entropy can be preferable when high recall is important. The advantages of convergent cross mapping and transfer entropy over other methods can increase with network size.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2021)