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

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Abstract

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.

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Authors and Affiliations

  1. Computer Science & Engineering, Arizona State University, Tempe, AZ, USA

    Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Anique Tahir & Huan Liu

  2. Northwestern Polytechnical University, Xi’an, China

    Qianru Wang

  3. Army Research Lab, Adelphi, USA

    Adrienne Raglin

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  1. Raha Moraffah
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Moraffah, R., Sheth, P., Karami, M. et al. Causal inference for time series analysis: problems, methods and evaluation. Knowl Inf Syst 63, 3041–3085 (2021). https://doi.org/10.1007/s10115-021-01621-0

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