Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.

https://www.mdpi.com/2504-4494/6/1/10/pdf?version=1642140414

Causal Discovery in Manufacturing: A Structured Literature Review

The convenient access to copious multifaceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have, therefore, witnessed great effort in developing causal learning algorithms aiming to help artificial intelligence (AI) achieve human-level intelligence. Due to the lack of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with Big Data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We, then, examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of Big Data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning. 

/pdf/evaluation-methods-and-measures-for-causal-learning-246dtk86.pdf

Evaluation Methods and Measures for Causal Learning Algorithms

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Fault diagnosis is critical for ensuring safe and stable chemical production. Correct identification of causal relationships among variables in large-scale chemical processes is a prerequisite for analyzing the root causes and propagation paths of faults. However, chemical process big data often exhibit nonlinearity and nonstationarity, and contain various forms of noise, rendering conventional causal discovery methods vulnerable. In this paper, a novel causal discovery method based on the causality-gated time series Transformer (CGTST) is proposed to address this challenge. By performing time series prediction using the Transformer-based model on the target variable, CGTST measures the causal strength by assessing the contribution of each variable to the prediction through the causality gate structure. Furthermore, a causal validation method based on permutation feature importance is proposed to eliminate spurious causal relationships and ensure robust results. To enhance the performance of causal discovery on nonlinear and nonstationary chemical process data, ensemble empirical mode decomposition is employed to reduce noise. The CGTST-based method is validated on three case studies: a continuous stirred-tank reactor, the Tennessee Eastman process, and a real-world continuous catalytic reforming process. Our findings demonstrate that the proposed method outperforms conventional causal discovery methods and holds promising prospects for industrial applications. 

Large-scale chemical process causal discovery from big data with Transformer-based deep learning

Abstract In March 2020, the COVID-19 pandemic forced over 1 billion learners to shift from face-to-face instruction to online learning. Seven months after it began, this transition became even more challenging for Filipino online learners. Eight typhoons struck the Philippines from October to November 2020. Two of these typhoons caused widespread flooding, utilities interruptions, property destruction, and loss of life. We examine how these severe weather conditions affected online learning participation of Filipino students pursuing their undergraduate and graduate studies. We used CausalImpact analysis to explore September 2020 to January 2021 data collected from the Moodle Learning Management System data of one university in the Philippines. We found that overall student online participation was significantly negatively affected by typhoons. However, the effect on participation in Assignments and Quizzes was not significant. These findings suggested that students continued to participate in activities that have a direct bearing on their final grades, rather than activities that had no impact on their course outcomes. 

/pdf/the-impact-of-extreme-weather-on-student-online-learning-1ep5wfh2.pdf

The impact of extreme weather on student online learning participation

Time series data are a collection of chronological observations which are generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting and clustering have been proposed to analyze this type of data. Time series data have been also used to study the effect of interventions overtime. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide an in-depth insight. These metrics and datasets can serve as benchmark for research in the field.

Causal inference for time series analysis: problems, methods and evaluation

Recommender systems suffer from biases that may misguide the system when learning user preferences. Under the causal lens, the user’s exposure to items can be seen as the treatment assignment, the ratings of the items are the observed outcome, and the different biases act as confounding factors. Therefore, to infer debiased preferences and to capture the causal relationship between exposure and the observed ratings, it is essential to account for any hidden confounders. To this end, we propose a novel causal disentanglement framework that decomposes latent representations into three independent factors, responsible for (a) modeling the exposure of an item, (b) predicting ratings, and (c) controlling for hidden confounders. Experiments on real-world datasets validate the effectiveness of the proposed Causal Disentanglement for DeBiased Recommendations (D2Rec) model in debiasing recommendations. 

Causal Disentanglement with Network Information for Debiased Recommendations

Causal inference is a critical task in various fields such as healthcare, economics, marketing and education. Recently, there have been significant advances through the application of machine learning techniques, especially deep neural networks. Unfortunately, to-date many of the proposed methods are evaluated on different (data, software/hardware, hyperparameter) setups and consequently it is nearly impossible to compare the efficacy of the available methods or reproduce results presented in original research manuscripts. In this paper, we propose a causal inference toolbox (CauseBox) that addresses the aforementioned problems. At the time of publication, the toolbox includes seven state of the art causal inference methods and two benchmark datasets. By providing convenient command-line and GUI-based interfaces, the CauseBox toolbox helps researchers fairly compare the state of the art methods in their chosen application context against benchmark datasets. The code is made public at github.com/paras2612/CauseBox.

CauseBox: A Causal Inference Toolbox for BenchmarkingTreatment Effect Estimators with Machine Learning Methods

Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields Over the years, different tasks such as classification, forecasting, and clustering have been proposed to analyze this type of data Time series data has been also used to study the effect of interventions over time Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task In this paper, we focus on two causal inference tasks, ie, treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide in-depth insight These metrics and datasets can serve as benchmarks for research in the field

Causal Inference for Time series Analysis: Problems, Methods and Evaluation

There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.

Paras Sheth

Papers

Causal inference for time series analysis: problems, methods and evaluation

Causal Disentanglement with Network Information for Debiased Recommendations

CauseBox: A Causal Inference Toolbox for BenchmarkingTreatment Effect Estimators with Machine Learning Methods

Causal Inference for Time series Analysis: Problems, Methods and Evaluation

Causal Learning for Socially Responsible AI