Methods and tools for causal discovery and causal inference

Nogueira, Ana Rita; Pugnana, Andrea; Ruggieri, Salvatore; Pedreschi, Dino; Gama, João

doi:10.1002/widm.1449

Cited by 69 publications

(45 citation statements)

References 119 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The retrospective design of this study remains a limitation, and the results reported may be due to observational biases and should not be assigned a causal interpretation. In particular, quantifying causal treatment effects from such observational data is an active area of research and such analysis is beyond the scope of this study 38,39 . The data gathering process is observational and whilst FCAL is collected routinely at all clinical interactions, subjects with more complicated disease are still likely to have more measurements available.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal Faecal Calprotectin Profiles Characterise Disease Course Heterogeneity in Crohn’s Disease

Constantine‐Cooke

Monterrubio-Gómez

Plevris

et al. 2022

Preprint

View full text Add to dashboard Cite

Background High faecal calprotectin is associated with poor outcomes in Crohn's disease. Monitoring of faecal calprotectin trajectories could characterise disease progression before severe complications occur. Aims We undertook an unbiased assessment of a retrospective incident Crohn's disease cohort to assess for inter-individual variability in faecal calprotectin levels over time. We aimed to explore whether latent classes of such profiles are associated with a composite endpoint consisting of surgery, hospitalisation, or Montreal behaviour progression and other clinical information. Methods Latent class mixed models were used to model faecal calprotectin trajectories within five years of diagnosis. Akaike information criterion, Bayesian information criterion, alluvial plots, and class-specific trajectories were used to decide the optimal number of classes. Log-rank tests of Kaplan-Meier estimators were used to test for associations between class membership and outcomes. Results Our study cohort comprised 365 subjects and 2856 faecal calprotectin measurements (median 7 per subject). Four latent classes were found and broadly described as a class with consistently high faecal calprotectin and three classes characterised by downward trends for calprotectin. Class membership was significantly associated with the composite endpoint, and separately, hospitalisation and Montreal disease progression, but not surgery. Early biologic therapy was strongly associated with class membership. Conclusions Our analysis provides a novel stratification approach for Crohn's disease patients based on faecal calprotectin trajectories. Characterising this heterogeneity helps to better understand different patterns of disease progression and to identify those with a higher risk of worse outcomes. Ultimately, this information will assist the design of more targeted interventions.

show abstract

Section: Discussionmentioning

confidence: 99%

Longitudinal Faecal Calprotectin Profiles Characterise Disease Course Heterogeneity in Crohn’s Disease

Constantine‐Cooke

Monterrubio-Gómez

Plevris

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Causation describes the relationship between one factor and another in a system [26]. The former factor is the cause of the latter factor, and the latter factor is the result of the former factor.…”

Section: Causal Inference (Ci)mentioning

confidence: 99%

A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

Today, neural network models are widely used to predict whether a person will develop diabetes in the future. However, for fuzzy inference engine and Adaptive Network-based Fuzzy Inference System (ANFIS), it costs a lot when the number of features is large, and the model will be more explainable if we use feature selection methods. In this paper, we modify the ANFIS model, combine ANFIS and neural network, and propose the ANFIS-NN model. We use SMOTE to address the imbalance between different classes, and use RFE and Casual Inference to do feature selection work. Then, we train an ANFIS model, and use a 5-layer neural network to replace the last layers to improve prediction accuracy. Data comparison experiments shows that our models get 0.9812 on accuracy, 0.9790 on G-mean, 0.9776 on F1 score on PIMA dataset using Casual Inference feature selection method, much better than traditional ANFIS; and our model works better on other diabetes datasets. The ANFIS-NN proposed in this paper can also be applied to other datasets to predict diabetes.

show abstract

“…Further, we assume a feature matrix vector

that contains all confounders , namely the variables that are causally influencing both the treatment and the outcome. Note that all confounders are observed, otherwise special causal effect learning methods need to be deployed [ 43 , 48 ]. Formally, the causal relations are represented graphically with Directed Acyclic Graphs (DAG), which means that the arrangement and orientation of the edges should not form any cycles.…”

Section: Proposed Approachmentioning

confidence: 99%

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Koutroulis

Mutlu

Kern

2022

Sensors

View full text Add to dashboard Cite

Critical infrastructure, such as water treatment facilities, largely relies on the effective functioning of industrial control systems (ICSs). Due to the wide adoption of high-speed network and digital infrastructure technologies, these systems are now highly interconnected not only to corporate networks but also to the public Internet, mostly for remote control and monitoring purposes. Sophisticated cyber-attacks may take advantage the increased interconnectedness or other security gaps of an ICS and infiltrate the system with devastating consequences to the economy, national security, and even human life. Due to the paramount importance of detecting and isolating these attacks, we propose an unsupervised anomaly detection approach that employs causal inference to construct a robust anomaly score in two phases. First, minimal domain knowledge via causal models helps identify critical interdependencies in the system, while univariate models contribute to individually learn the normal behavior of the system’s components. In the final phase, we employ the extreme studentized deviate (ESD) on the computed score to detect attacks and to exclude any irrelevant sensor signals. Our approach is validated on the widely used Secure Water Treatment (SWaT) benchmark, and it exhibits the highest F1 score with zero false alarms, which is extremely important for real-world deployment.

show abstract

Methods and tools for causal discovery and causal inference

Cited by 69 publications

References 119 publications

Longitudinal Faecal Calprotectin Profiles Characterise Disease Course Heterogeneity in Crohn’s Disease

Longitudinal Faecal Calprotectin Profiles Characterise Disease Course Heterogeneity in Crohn’s Disease

A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Contact Info

Product

Resources

About