Fig 1. Active learning framework for network reconstruction.

We first estimate our belief over candidate graph structures based on the initial data set that contains observational and/or intervention samples. Then, we iteratively acquire new data instances by carrying out the optimal intervention experiment predicted to cause the largest change in our belief (in expectation) and updating the belief. The final belief is summarized into a predicted network via Bayesian model averaging.