Targeted Cause Discovery with Data-Driven Learning

We propose a novel machine learning approach for inferring causal variables of a target variable from observations. Our goal is to identify both direct and indirect causes within a system, thereby efficiently regulating the target variable when the difficulty and cost of intervening on each causal variable vary. Our method employs a neural network trained to identify causality through supervised learning on simulated data. By implementing a local-inference strategy, we achieve linear complexity with respect to the number of variables, efficiently scaling up to thousands of variables. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks, outperforming existing causal discovery methods that primarily focus on direct causality. We validate our model's generalization capability across novel graph structures and generating mechanisms, including gene regulatory networks of E. coli and the human K562 cell line. Implementation codes are available at https://github.com/snu-mllab/Targeted-Cause-Discovery.