title: Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.
abstract: Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.
authors.affiliation: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los AngelesLos Angeles, CA, USA; Silva Laboratory, Departments of Neurobiology, Psychiatry, and Psychology, Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los AngelesLos Angeles, CA, USA.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Matiasz
authors.affiliation: Silva Laboratory, Departments of Neurobiology, Psychiatry, and Psychology, Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los AngelesLos Angeles, CA, USA; Department of Computer Science, Scalable Analytics Institute, University of California, Los AngelesLos Angeles, CA, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Wood
authors.affiliation: Department of Computer Science, Scalable Analytics Institute, University of California, Los Angeles Los Angeles, CA, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Silva Laboratory, Departments of Neurobiology, Psychiatry, and Psychology, Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles Los Angeles, CA, USA.
authors.forename: Alcino J
authors.initials: AJ
authors.lastname: Silva
authors.affiliation: Medical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles Los Angeles, CA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Hsu
keywords: causal graph; epistemology; experiment planning; information gain; research map; uncertainty quantification