Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
This method addresses causal discovery for researchers in machine learning and statistics, representing an incremental advancement by integrating adversarial techniques into existing frameworks.
The paper tackles the problem of discovering causal structures from observational data by introducing Structural Agnostic Modeling (SAM), which uses adversarial learning and a combination of constraints to optimize graph structures, achieving validation on synthetic and real datasets.
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims to find the underlying causal structure from observational data. The approach is based on a game between different players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the generated data against the original data. A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the optimization of the graph structure and parameters through stochastic gradient descent. SAM is extensively experimentally validated on synthetic and real data.