Directed Acyclic Graphs

Directed Acyclic Graphs, or DAGs, are visual and mathematical representations used in causal inference to map out the relationships between different market variables. In crypto and derivatives, nodes represent variables such as asset prices, order flow, or funding rates, while directed edges indicate the causal influence one variable has on another.

The term acyclic signifies that the path of influence cannot loop back on itself, preventing logical contradictions in the model. By constructing a DAG, analysts can identify confounding variables that might distort the perceived relationship between an intervention and an outcome.

This framework helps in designing more accurate trading algorithms by explicitly defining the structural dependencies of the market. It provides a clear blueprint for understanding how exogenous shocks propagate through a financial system.

Mastering DAGs allows for the systematic deconstruction of complex market behaviors into manageable, testable causal paths.