EFfECT-RL: Enabling Framework for Establishing Causality and Triggering engagement through RL

Skill-based games offer an exceptional avenue for entertainment, fostering self-esteem, relaxation, and social satisfaction. Engagement in online skill gaming platforms is however heavily dependent on the outcomes and experience (e.g., wins/losses). Understanding the factors driving increased engagement is crucial within skill gaming platforms. In this study, we aim to address two key questions: (1) "What factors are driving users to increase their engagement?" and (2) "How can we personalize users journey accordingly to further optimize their engagement?". In skill gaming platforms, the impact of causal relationships often manifests with a delay, which varies significantly as users? personas evolve. Without a detailed information on treatments (such as timing and frequency), estimating the impact of a causal-treatment-effect in a highly volatile game-play data becomes exceedingly challenging. This work proposes a framework called EFfECT-RL that establishes causal discovery by integrating change-point detection and explainable K-means clustering, while leveraging users' game-play and transactional-data. Unlike existing methods which were unable to detect causal-effects in extremely volatile-data, EFfECT-RL generates threshold-trees (~ 79% accuracy) elucidating causal-relationships. Once the causal relationship is established, we personalize treatments by developing a novel offline deep reinforcement learning-based approach. Our online recommendations show a 3% improvement in user engagement (platform-centric) with 70% relevancy (user-centric).