Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
This addresses the challenge of efficient causal discovery for researchers and practitioners in fields requiring causal inference from observational data, offering a scalable solution with theoretical guarantees.
The paper tackles the problem of inferring direct causal parents from observational data, proposing an algorithm that scales to large graphs and handles partially nonlinear relationships, demonstrating significant improvements over established methods.
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work exploits stability of regression coefficients or invariance properties of models across different experimental conditions for reconstructing the full causal graph. These approaches generally do not scale well with the number of the explanatory variables and are difficult to extend to nonlinear relationships. Contrary to existing work, we propose an approach which even works for observational data alone, while still offering theoretical guarantees including the case of partially nonlinear relationships. Our algorithm requires only one estimation for each variable and in our experiments we apply our causal discovery algorithm even to large graphs, demonstrating significant improvements compared to well established approaches.