MetaCaDI: A Meta-Learning Framework for Scalable Causal Discovery with Unknown Interventions
This work addresses the problem of scalable causal discovery with unknown interventions for researchers and practitioners in fields like biology, where data is scarce and costly.
The paper tackles the challenge of discovering causal graphs and unknown interventions in complex systems with high data collection costs by introducing MetaCaDI, a meta-learning framework that uses Bayesian methods and analytical adaptation. It significantly outperforms state-of-the-art methods, excelling at causal graph recovery and identifying intervention targets from as few as 10 data instances.
Uncovering the underlying causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions. We introduce MetaCaDI, the first framework to cast the joint discovery of a causal graph and unknown interventions as a meta-learning problem. MetaCaDI is a Bayesian framework that learns a shared causal graph structure across multiple experiments and is optimized to rapidly adapt to new, few-shot intervention target prediction tasks. A key innovation is our model's analytical adaptation, which uses a closed-form solution to bypass expensive and potentially unstable gradient-based bilevel optimization. Extensive experiments on synthetic and complex gene expression data demonstrate that MetaCaDI significantly outperforms state-of-the-art methods. It excels at both causal graph recovery and identifying intervention targets from as few as 10 data instances, proving its robustness in data-scarce scenarios.