Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, including neural networks, operate as black boxes: despite performing suitably well as predictive models, they are not able to identify causal associations within the data. For data-driven system to approach human-level intelligence in generating effective maintenance strategies, it is integral to discover hidden knowledge in the operational data. In this paper, we apply deep learning to discover causal relationships between multiple features (confounders) in SCADA data for faults in various sub-components from an operational turbine using convolutional neural networks (CNNs) with attention. Our technique overcomes the black box nature of conventional deep learners and identifies hidden confounders in the data through the use of temporal causal graphs. We demonstrate the effects of SCADA features on a wind turbine's operational status, and show that our technique contributes to explainable AI for wind energy applications by providing transparent and interpretable decision support

InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity

Deciphering brain network topology can enhance the depth of neuroscientific knowledge and facilitate the development of neural engineering methods. Effective connectivity, a measure of brain network dynamics, is particularly useful for investigating the directional influences among different brain regions. In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series. The proposed method estimates the magnitude of directional information flow (dIF) among EEG processes by measuring the loss of model inference resulting from the shuffling of the time order of the original time series. To evaluate the feasibility of InfoFlowNet, we conducted experiments using a synthetic time series and two EEG datasets. The results demonstrate that InfoFlowNet can extract time-varying causal relationships among processes, reflected in the fluctuation of dIF values. Compared with the Granger causality model and temporal causal discovery framework, InfoFlowNet can identify more significant causal edges underlying EEG processes while maintaining an acceptable computation time. Our work demonstrates the potential of InfoFlowNet for analyzing effective connectivity in EEG data. The findings highlight the importance of effective connectivity in understanding the complex dynamics of the brain network

Approximate and Situated Causality in Deep Learning

Altres ajuts: ICREA Academia 2019, and "AppPhil: Applied Philosophy for the Value-Design of Social Networks Apps" project, funded by Caixabank in Recercaixa2017.Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes