Direct Estimation of Differences in Causal Graphs

Abstract

We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models with a shared topological order given i.i.d. samples from each model. This is of interest for example in genomics, where changes in the structure or edge weights of the underlying causal graphs reflect alterations in the gene regulatory networks. We here provide the first provably consistent method for directly estimating the differences in a pair of causal DAGs without separately learning two possibly large and dense DAG models and computing their difference. Our two-step algorithm first uses invariance tests between regression coefficients of the two data sets to estimate the skeleton of the difference graph and then orients some of the edges using invariance tests between regression residual variances. We demonstrate the properties of our method through a simulation study and apply it to the analysis of gene expression data from ovarian cancer and during T-cell activation.

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• Geometry of the faithfulness assumption in causal inference
• Multiple Testing and Error Control in Gaussian Graphical Model Selection
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Cited by in corpus (2)

• Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
• Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models