Physical System for Non Time Sequence Data
This work addresses causal inference for physical systems, offering a method that enforces acyclicity and reduces complexity, but it appears incremental as it builds on existing Jacobian-based techniques.
The authors tackled the problem of learning causal structures from non-time-series data by extending a Jacobian-based neural network approach to physical systems, achieving a significant reduction in computational complexity for graph search space.
We propose a novelty approach to connect machine learning to causal structure learning by jacobian matrix of neural network w.r.t. input variables. In this paper, we extend the jacobian-based approach to physical system which is the method human explore and reason the world and it is the highest level of causality. By functions fitting with Neural ODE, we can read out causal structure from functions. This method also enforces a important acylicity constraint on continuous adjacency matrix of graph nodes and significantly reduce the computational complexity of search space of graph.