My Kingdom for a Causal Algorithm

This chapter delves into the critical need for reliable and interpretable causal algorithms in the contemporary landscape of artificial intelligence. Drawing parallels with Richard III’s desperate cry for a horse, the chapter underscores the pivotal role causal algorithms play in diverse fields, including healthcare, finance, and policy. The challenges in developing effective causal algorithms mirror the complexities faced by regression models. The pursuit involves addressing issues such as confounding variables, spurious correlations, and selection bias while ensuring transparency and interpretability. The multifaceted nature of causality requires a nuanced approach, with causal algorithms ranging from statistical methods to advanced techniques such as structural equation modeling and Bayesian networks. The chapter explores the intricate process of inferring causal relationships, emphasizing the importance of interdisciplinary collaboration and a comprehensive understanding of causality. It concludes by offering a philosophical exploration of causal understanding, tracing its evolution through history, debates, and the dynamic interplay between philosophy and computational science.