Amortized Active Causal Induction with Deep Reinforcement Learning

Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster

Keywords: Active Causal Structure Learning, Adaptive Intervention Design, Reinforcement Learning

TL;DR: We propose an amortized and adaptive intervention strategy that results in a sample efficient estimate of the true causal graph on the distribution of training environment as well as on test-time environments with distribution shifts.

Abstract: We present Causal Amortized Active Structure Learning (CAASL), an active intervention design policy that can select interventions that are adaptive, real-time and that does not require access to the likelihood. This policy, an amortized network based on the transformer, is trained with reinforcement learning on a simulator of the design environment, and a reward function that measures how close the true causal graph is to a causal graph posterior inferred from the gathered data. On synthetic data and a single-cell gene expression simulator, we demonstrate empirically that the data acquired through our policy results in a better estimate of the underlying causal graph than alternative strategies. Our design policy successfully achieves amortized intervention design on the distribution of the training environment while also generalizing well to distribution shifts in test-time design environments. Further, our policy also demonstrates excellent zero-shot generalization to design environments with dimensionality higher than that during training, and to intervention types that it has not been trained on.

Primary Area: Active learning

Submission Number: 11837