( Image credit: TCDF )
This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling.
We consider causal discovery from time series using conditional independence (CI) based network learning algorithms such as the PC algorithm.
Combining the local permutation scheme with the kernel tests leads to better calibration, but suffers in power.
We next utilize the augmented form to develop a masked structure learning method that can be efficiently trained using gradient-based optimization methods, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix.
This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.
We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data.
Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph.