A Survey on Causal Discovery: Theory and Practice

Zanga, Alessio; Özkırımlı, Elif; Stella, Fabio

doi:10.1016/j.ijar.2022.09.004

Cited by 40 publications

(7 citation statements)

References 42 publications

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“…Due to the presence of reflected waves, the PPG waveform of each cycle is not symmetrical, and the reflected wave speed is related to vascular aging, peripheral arterial compliance, arteriosclerosis, circumferential stress of the artery wall, etc. [15,16] . By analyzing the symmetry of PPG waveform, these factors can be reflected to a certain extent.…”

Section: Feature Extractionmentioning

confidence: 99%

An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference

Song,

Ding

2024

Conn Health Telemed

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Aim: We propose to examine the causal relationship between the noninvasive features represented by pulse transit time (PTT) and blood pressure (BP), with the aim of mitigating the impact of confounding factor(s) and thus improving the performance of BP estimation. Methods: We identified the causal graph of BP and the important noninvasive features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) via fast causal inference (FCI) algorithm, with the orientations of the edges in the causal graph being determined by the causal generative neural networks (CGNN) algorithm. With the knowledge obtained from the causal graph, we further used hierarchical regression model to estimate BP, and validated the proposed method on 17 subjects. Results: We found that the obtained causal graph was almost consistent with the prior knowledge, and heart rate (HR) was one of the main confounding factors of PTT and BP. Incorporating HR into the hierarchical regression model to eliminate its confounding effect on the PTT-based BP estimation, the mean value of SBP and DBP estimation was improved by 1.27 and 1.89 mmHg, respectively, and the mean absolute difference (MAD) was improved by 2.28 and 3.60 mmHg, respectively. Conclusion: Causal inference-based method has the potential to clarify the causal relationship between BP and related wearable noninvasive features, which can further shed light on developing new methods for cuffless BP with acceptable accuracy.

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Section: Feature Extractionmentioning

confidence: 99%

An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference

Song,

Ding

2024

Conn Health Telemed

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“…Causal discovery (CD) refers to the process of identifying and understanding the causal relationships between variables in a system. The objective is to uncover the cause-and-effect relationships that exist between different factors or variables within a data set or a real-world system [38].…”

Section: Preliminary: Causal Discoverymentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

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In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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“…Em outras palavras, uma aresta direcionada X −→ Y estabelece uma relac ¸ão de causa e efeito entre a causa X e seu efeito Y . Dado um conjunto G que consiste em todos os grafos definidos pelas variáveis V de um conjunto de dados D, o problema da descoberta causal é encontrar o grafo G * que seja uma possível explicac ¸ão para D [24].…”

Section: A Descoberta Causalunclassified

Explorando causalidade na seleção de variáveis para previsão de séries temporais multivariadas

Lucas,

Mendes,

Guimarães

2023

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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A previsão de séries temporais desempenha um papel fundamental em diversos setores, fornecendo insights valiosos para o planejamento e tomada de decisões em várias áreas de aplicação. A seleção de características em series temporais e, por sua vez, um componente crucial no pipeline de aprendizado de máquina. Ao selecionar cuidadosamente um conjunto apropriado de variáveis, os modelos de aprendizado de máquina podem aumentar a precisão da previsão, reduzir o tempo de processamento e melhorar a interpretabilidade. Neste estudo, comparamos o método de descoberta causal PCMCI (Peter and Clark Momentary Conditional Independence) com outros métodos de seleção de características não causais, como Correlação, Algoritmos Genéticos e regularização LASSO. Três conjuntos de dados do mundo real são usados nos experimentos de acordo com tres resultados principais: tamanho do gráfico gerado (numero de variáveis selecionadas), tempo de execução e precisão da previsão em diferentes horizontes de 1, 3 e 5 passos a frente. Os resultados mostram a capacidade do PCMCI em ` gerar modelos de previsão mais enxutos, proporcionando maior interpretabilidade e reduzindo a suscetibilidade ao sobre-ajuste.

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A Survey on Causal Discovery: Theory and Practice

Cited by 40 publications

References 42 publications

An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference

An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Explorando causalidade na seleção de variáveis para previsão de séries temporais multivariadas

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