Relational Causal Model

Relational causal models aim to represent and reason about complex systems where interconnected units mutually influence each other, going beyond traditional causal models that assume acyclicity. Current research focuses on developing model architectures and algorithms that can handle cyclic relationships, often drawing inspiration from human cognitive processes like memory and relational thinking, and employing techniques like relational acyclification and successor representations. These advancements are improving performance in tasks such as question answering, speech recognition, and the design of complex systems by enabling more accurate modeling of real-world phenomena with feedback loops and emergent properties.