The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.

Democracy requires students to choose policy positions based on evidence, yet confirmation bias prevents them from doing so. As a preliminary step in building a policy reasoning tutor, this study identifies where bias occurs during the search and analysis of evidence in a policy reasoning task. 60 university students played an on-line game in which they chose which of four policies would increase school performance. The between-subjects design compared a free search group who searched for evidence in google-like environment, to a sequential presentation group who read all available evidence, and manipulated whether the evidence confirmed or disconfirmed students' prior beliefs. The study measured the impact on students' evidence-based recommendations, their change in beliefs, and their recall of the evidence. Results showed that students did not cherry-pick evidence nor discount disconfirming evidence. However, students' extreme confidence in their initial beliefs usually prevented them from changing position, and they mistakenly recalled the evidence as confirming their beliefs. The results suggest that a policy tutor should focus on evidence synthesis and making recommendations based on explicit evidence.

We present a Bayesian search algorithm for learning the structure of latent variable models of continuous variables. We stress the importance of applying search operators designed especially for the parametric family used in our models. This is performed by searching for subsets of the observed variables whose covariance matrix can be represented as a sum of a matrix of low rank and a diagonal matrix of residuals. The resulting search procedure is relatively efficient, since the main search operator has a branch factor that grows linearly with the number of variables. The resulting models are often simpler and give a better fit than models based on generalizations of factor analysis or those derived from standard hill-climbing methods.

We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems.

Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the d-separations that define the graphical structure. This paper describes new distribution-free techniques for identifying d-separations in continuous latent variable models when non-linear dependencies are allowed among hidden variables.

MOTIVATION: One approach to inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization is to estimate the associations of gene expression levels measured in repeated samples. The associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. Although these procedures have been successfully applied to other areas, their validity when applied to microarray measurements has yet to be tested. RESULTS: This paper describes an elementary statistical difficulty for all such procedures, no matter whether based on Bayesian updating, conditional independence testing, or other machine learning procedures such as simulated annealing or neural net pruning. The difficulty obtains if a number of cells from a common population are aggregated in a measurement of expression levels. Although there are special cases where the conditional associations are preserved under aggregation, in general inference of genetic regulatory structure based on conditional association is unwarranted

Structural equation models with latent variables are used widely in psychometrics, econometrics, and sociology to explore the causal relations among latent variables. Since such models often involve dozens of variables, the number of theoretically feasible alternatives can be astronomical. Without computational aids with which to search such a space, researchers can only explore a handful of alternative models. We describe a procedure that can find information about the causal structure among latent, or unmeasured variables. the procedure is asymptotically reliable, feasible on data sets with as many as a hundred variables, and has already proved useful in modeling an empirical data set collected by the U.S. Navy. © 1992 John Wiley & Sons, Inc.