Advances in Computational Statistics, Causal Inference and Data Science—Interface and Applications—Theory and Applications

Dear Colleagues, 

This leading Special Issue promotes and stimulates research at the interface of computational statistics, causal inference and data science.

Causal inference (CI) is well discussed as a methodology to uncover causal relationships in real-world problems. Given the explosion in the volume and types of data available to support research outcomes, health economics, and epidemiology, it has been argued and shown that machine learning now also brings a renewed vitality to the area of causal inference, with new ideas in CI able to promote new advancements in machine learning (ML)—despite ML methods being traditionally used for classification and prediction, not causal inference, even though the prediction capabilities of ML are widely accepted. It is noteworthy that the use of machine and deep learning (DL) for causal inference is still evolving. For example, in natural language processing, researchers have recently looked to enhancing natural language processing tasks with causal inference.

Predictive analytics are rapidly being upgraded using machine learning methods, however, a question remains as to how to draw causal inference from observational data. Can machine learning help? ML would be an effective tool for hypothesis generation, as ML’s core strength is in identifying correlational structures in observational data. Once identified, these structures can be tested with usual causal modelling approaches. Can we use machine learning (deep learning) to estimate causal models directly? Can we incorporate a statistical perspective into data science? Such questions are especially relevant in applications for stochastic processes (COVID-19, diseases) and big data such as electronic health records (ehRs) and clinical care, precision medicine, public and global health, digital health, and even in randomized controlled trials (RCTs).

A wide range of theoretical and practical applications are welcome, including, but not limited to, the following topics:

• Connections between classical statistical methods and machine learning, deep learning models.
• Causal inference and data science interface with applications in observational studies, RCTs, surveys and digital health, ecology, climate research and precision medicine.
• Modelling causality and explainability in digital health and large data problems.
• Visualization via directed acyclic graphs (DAGs), multiple correspondence analysis (MCA).
• Explainability versus causality in clinical care, precision medicine, public and global health, digital health, RCTs, survey research, and precision medicine.
• Data-driven causal analysis of time series.
• Novel frameworks to strengthen use of DL with causal inference and vice versa with applications for stochastic processes (COVID-19, diseases) and big data (electronic health records (ehRs), etc.).

We would like to offer an opportunity to researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area and stimulate vigorous discourse.

Prof. Dr. Irene Hudson
Prof. Dr. Eric J. Beh
Dr. Sharon Lee
Guest Editors

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• statistical/causal inferences
• data science
• explainability
• surveys
• digital health
• RCTs