ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

Understand

Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.

• However, this is often challenging in practice due to limited interventional data.
• Given that randomized experiments are usually expensive to perform, we propose a general framework and theory based on optimal Bayesian experimental design to select experiments for targeted causal discovery.
• That is, we assume the experimenter is interested in learning some function of the unknown graph (e.g., all descendants of a target node) subject to design constraints such as limits on the number of samples and rounds of experimentation.