Title: Machine learning made easy: a beginner's guide for causal inference and discovery methods using Python

Authors: Irfan Saleem; Ali Irfan

Addresses: Faculty of Business, Sohar University, Sohar, PC 311, Sultanate of Oman ' Department of Computer Science IU International University of Applied Sciences, 53604, Bad Honnef, Germany

Abstract: Machine learning is widely recognised and extensively used for data modelling and prediction across fields, including business and healthcare, to name a few of them, for informed decision-making. Numerous machine learning algorithms have been devised and deployed across multiple programming languages throughout the preceding decades for causal inference and discovery. This research, however, briefly introduces causal inference and discovery methods, accompanied by Python code for beginners. First, this study talks about machine learning in brief. Then, this study differentiates between causal discovery and causal inference. Thirdly, the study aims to describe popular machine-learning methods. Finally, this paper demonstrates the practical uses of these causal inference and discovery packages in Python. The study has recommended future research and implications for using machine learning methods.

Keywords: Python; machine learning; causal discovery (CD); causal inference (CI); linear regression; Peter-Clark (PC) algorithm; artificial intelligence.

DOI: 10.1504/IJDATS.2025.144962

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.1, pp.36 - 53

Received: 07 Mar 2024
Accepted: 24 May 2024
Published online: 14 Mar 2025 *

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