Citation:

Rajesh Sura, 2025. "The Role of Causal Inference in Business Decision-Making and A/B Testing at Scale", ESP Journal of Engineering & Technology Advancements  5(2): 99-106.

Abstract:

Business analytics depends heavily on causal inference within data-driven strategy because this technique helps organizations advance from generic correlations to strong cause-and-effect relationships. A/B testing has established itself as the principal method for conducting scalable causal analysis because digital experimentation continues to grow rapidly. The review explores the conceptual bases together with practical applications and experimental approaches, and present-day difficulties regarding business-oriented causal inference specifically within A/B testing scalability. It covers experimental validity techniques and methods to control confounding variables and approaches for managing inconsistent treatment responses, and connections of machine learning techniques to causal evaluation methods. It uses visuals together with simulated experimental results and demonstrates their application to real-world scenarios within the text. Thus, it described upcoming research avenues centered around individualization practices, together with observation and experimentation protocols, as well as moral standards within algorithm-driven choices.

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Keywords:

A/B Testing, Business Analytics, Causal Inference, Data Science, Decision Science, Experimental Design, Personalization, Propensity Score, Randomized Controlled Trials (RCT), Treatment Effect.