Development of optimised causal AI and its application in retail banking

Causal artificial intelligence (AI) has emerged as a promising approach in machine learning (ML), as it considers not only correlations but also cause-and-effect relationships in data, resulting in more human-like decision making. The pivotal stages within causal AI involve causal discovery and inferencing, each playing a crucial role in extracting meaningful insights from the data. In the realm of causal discovery, various algorithms have been developed to uncover the underlying cause-and-effect structures within data sets. A notable limitation, however, surfaces when attempting to apply these algorithms to data sets characterised by binary variables. This constraint prompts a crucial examination of the current methodologies and calls for innovative solutions that can seamlessly navigate the complexities of binary variable data sets. This paper proposes an optimised causal discovery algorithm that is integrated with the causal inference method based on the estimation of conditional average treatment effects (CATE) scores. The results present the potential of causal AI in terms of incremental impact on the predictive capability of AI/ML models. And the incremental impact is elucidated by comparing conventional propensity-based modelling and causal AI-based modelling by means of a use case in the field of retail banking.