A Meta-Learning Approach to Causal Inference

Predicting the effect of unseen interventions is at the heart of many scientific endeavours. While causal discovery is often used to answer these causal questions, it involves learning a full causal model, not tailored to the specific goal of predicting unseen interventions, and operates under stringent assumptions. We introduce a novel method based on meta-learning that predicts interventional effects without explicitly assuming a causal model. Our preliminary results on synthetic data show that it can provide good generalization to unseen interventions, and it even compares favorably to a causal discovery method. Our model-agnostic method opens up many avenues for future exploration, particularly for settings where causal discovery cannot be applied.