Leveraging causal Machine Learning for informed decision-making: a collaborative and iterative approach


Friday, June 7, 2024


10:55 am


Phoenix Ballroom B


In the field of Artificial Intelligence and Machine Learning (AI/ML), a significant challenge lies not only in developing accurate models but also in effectively utilizing them to drive business decisions. This presentation aims to discuss the application of causal ML techniques in influencing important business decisions. By leveraging causal ML, we can uncover the causal relationships between variables, enabling us to understand the impact of various factors on business outcomes. This understanding empowers organizations to make informed decisions and optimize their strategies for success.

Furthermore, this presentation will highlight the importance of engaging business partners from the outset of the AI/ML implementation process. By actively involving them, we gain valuable insights into their existing manual decision-making processes. This understanding allows us to develop an iterative plan that aligns with their needs and preferences, ensuring their comfort and buy-in throughout the implementation journey. Additionally, a rigorous test and learn approach is designed to measure the success of the implemented AI/ML solution, providing tangible evidence of its effectiveness in driving business decisions. This collaborative and iterative approach not only enhances the adoption of AI/ML but also fosters a culture of continuous improvement and data-driven decision-making within organizations.

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