Tree-based additive noise directed acyclic graphical models for nonlinear causal discovery with interactions

Directed acyclic graphical models with additive noises are essential in nonlinear causal discovery and have numerous applications in various domains, such as social science and systems biology. Most such models further assume that structural causal functions are additive to ensure causal identifiability and computational feasibility, which may be too restrictive in the presence of causal interactions. Some methods consider general nonlinear causal functions represented by, for example, Gaussian processes and neural networks, to accommodate interactions. However, they are either computationally intensive or lack interpretability. We propose a highly interpretable and computationally feasible approach using trees to incorporate interactions in nonlinear causal discovery, termed tree-based additive noise models. The nature of the tree construction leads to piecewise constant causal functions, making existing causal identifiability results of additive noise models with continuous and smooth causal functions inapplicable. Therefore, we provide new conditions under which the proposed model is identifiable. We develop a recursive algorithm for source node identification and a score-based ordering search algorithm. Through extensive simulations, we demonstrate the utility of the proposed model and algorithms benchmarking against existing additive noise models, especially when there are strong causal interactions. Our method is applied to infer a protein-protein interaction network for breast cancer, where proteins may form protein complexes to perform their functions.