Orthogonal Structure Search for Efficient Causal Discovery from Observational Data

A more formal discussion explanatory variables is of high practical importance is provided in Section 2. in many disciplines. Recent work exploits stability of regression coefficients or invariance However most state of the art methods suffer from scalability properties of models across different experimental problems since they scan all potential subsets of variables conditions for reconstructing the full causal and test whether the conditional distribution of Y given graph. These approaches generally do not scale a subset of variables is invariant across all environments well with the number of the explanatory variables (Peters et al., 2016) . This search is hence exponential in and are difficult to extend to nonlinear relationships. the number of covariates; the methods, while maintaining Contrary to existing work, we propose an appealing theoretical guarantees, are thus already computationally approach which even works for observational data hard for graphs of ten variables, and get infeasible alone, while still offering theoretical guarantees for larger graphs, unless one resorts to heuristic procedures.