Differentiable Multi-Target Causal Bayesian Experimental Design
Panagiotis Tigas,u00a0Yashas Annadani,u00a0Desi R. Ivanova,u00a0Andrew Jesson,u00a0Yarin Gal,u00a0Adam Foster,u00a0Stefan Bauer
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting u2014 a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-value pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.
