Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model

Abstract

A high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causal Artificial Intelligence to high-stakes decisions, aiming to encode causal assumptions based on human-like intelligence, and thereby produce interpretable and argumentative knowledge. We develop a Causal Bayesian Networks model based on causal science using $d$ -separation and do-operations to discover the causal graph aligned with cognitive understanding. Causal odd ratios are used to measure the causal assumptions integrated with the real-world data to prove the proposed causal model compatibility. Causal effect relationships in the model are verified based on causal P-values and causal confident intervals and approved less than 1% by random chance. It shows that the causal model can encode cognitive understanding as precise, robust relationships. The concept of model design allows software agents to imitate human intelligence by inferring potential knowledge and be employed in high-stakes decision applications.

Keywords

• Artificial intelligence
• counterfactuals
• causal science
• do-calculus
• causal inference
• cognitive computing