Developing Causal Reasoning-Specific Optimization Techniques

EasyChair Preprint 15144

Abstract

Causal reasoning plays a crucial role in various fields, enabling more accurate decision-making by distinguishing between correlation and causation. Despite advancements in causal inference methods, traditional optimization techniques often fall short in addressing the complexities inherent in causal models, such as non-linearities, identifiability issues, and data scarcity. This paper explores the development of causal reasoning-specific optimization techniques designed to enhance the performance and reliability of causal models. We propose tailored objective functions that incorporate causal structure constraints and highlight the significance of intervention-based optimization approaches. Additionally, we investigate the application of gradient-based methods and Bayesian optimization in causal contexts. Through an analysis of diverse applications—ranging from healthcare and economics to artificial intelligence and marketing—we demonstrate the practical implications and benefits of these specialized techniques. Finally, we address future directions and challenges in integrating causal reasoning with machine learning, emphasizing the need for ethical considerations in the optimization process. This work aims to contribute to the growing body of knowledge in causal inference and optimization, providing a foundation for more effective and ethically responsible decision-making frameworks.

Keyphrases: Optimization Techniques, causal inference, causal reasoning, machine learning, personalized medicine

@booklet{EasyChair:15144,
  author    = {Docas Akinyele and David Ray},
  title     = {Developing Causal Reasoning-Specific Optimization Techniques},
  howpublished = {EasyChair Preprint 15144},
  year      = {EasyChair, 2024}}