A Survey of Learning Causality with Data

Guo, Ruocheng; Cheng, Lu; Li, Jundong; Hahn, P. Richard; Liu, Huan

doi:10.1145/3397269

Cited by 210 publications

(213 citation statements)

References 93 publications

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“…The study [220] empirically corroborated these predictions, thus establishing an intriguing bridge between the structure of learning problems and certain physical properties (cause-effect direction) of real-world data generating processes. It also led to a range of follow-up work [32], [78], [97], [114], [115], [152], [153], [156], [167], [195], [204], [243], [263], [267], [277], [278], [281], complementing the studies of Bareinboim and Pearl [14], [185], and it inspired a thread of work in the statistics community exploiting invariance for causal discovery and other tasks [105], [106], [114], [187], [191].…”

Section: A Semisupervised Learningmentioning

confidence: 99%

Toward Causal Representation Learning

et al. 2021

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The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of highlevel causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.

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Section: A Semisupervised Learningmentioning

confidence: 99%

Toward Causal Representation Learning

et al. 2021

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“…One classical way of reasoning causality is via randomized controlled trial by evaluating the outcomes from the treatment group and control group, which is however costly and time-consuming. Recently, learning causality from observational data has attracted much attention [229]. There are two well-known causal models used for learning causality, i.e., the structural causal models and the potential outcome framework (a.k.a.…”

Section: Reasoningmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

459

168

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In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

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“…Causality exists in machine learning [38]. The authors of [42] learned causality between text features and vocabulary in recurrent neural networks.…”

Section: Causal Inferencementioning

confidence: 99%

“…Causal inference was used to explore sensitive spectral bands for the assessment of TP concentration in this paper. Causal inference [38][39][40] refers to the process of seeking a causal relationship between a cause and its effect. It is a useful tool for explanatory analysis and has been introduced into machine learning to confirm the correlation between variables and outcomes [41,42].…”

Section: Introductionmentioning

confidence: 99%

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

Ding

et al. 2020

Water

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The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with R2 > 0.73, root mean squared error (RMSE) < 0.037 mg/L in Lake Okeechobee and R2 > 0.73, RMSE < 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way.

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A Survey of Learning Causality with Data

Cited by 210 publications

References 93 publications

Toward Causal Representation Learning

Toward Causal Representation Learning

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

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