Learning Neural Causal Models with Active Interventions
This work addresses the challenge of causal discovery for researchers in science and machine learning, offering an incremental improvement in efficiency for intervention-based methods.
The paper tackles the problem of learning causal structures from data by introducing an active intervention targeting method that reduces the number of interactions needed compared to random targeting, demonstrating superior performance on benchmarks from simulated to real-world data.
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or fixed interventional origin. In this work, we introduce an active intervention targeting (AIT) method which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting and is applicable for both discrete and continuous optimization formulations of learning the underlying directed acyclic graph (DAG) from data. We examine the proposed method across multiple frameworks in a wide range of settings and demonstrate superior performance on multiple benchmarks from simulated to real-world data.