2021/03/28 by Michele Zemplenyi, Zemplenyi, Michele, Jeffrey W. Miller +1 · 2 citations
Computer Science · Decision Sciences · Mathematics · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Methodology (stat.ME) #Optimal Experimental Design Methods #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2103.15229
openalex publication_date 2021/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since interventional experiments can be costly, it is preferable to select interventions that yield the maximum amount of information about a system. We propose a novel Bayesian method for optimal experimental design by sequentially selecting interventions that minimize the expected posterior entropy as rapidly as possible. A key feature is that the method can be implemented by computing simple summaries of the current posterior, avoiding the computationally burdensome task of repeatedly performing posterior inference on hypothetical future datasets drawn from the posterior predictive. After deriving the method in a general setting, we apply it to the problem of inferring causal networks. We present a series of simulation studies in which we find that the proposed method performs favorably compared to existing alternative methods. Finally, we apply the method to real and simulated data from a protein-signaling network.