Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially useful for synthesising evidence or belief concerning a complex intervention, assessing the sensitivity of outcomes to different situations or contextual frameworks and framing decision problems that involve alternative types of intervention.…

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

Data from single case designs (SCDs) have traditionally been analyzed by visual inspection rather than statistical models. As a consequence, effect sizes have been of little interest. Lately, some effect-size estimators have been proposed, but most are either (i) nonparametric, and/or (ii) based on an analogy incompatible with effect sizes from…

In intervention studies, it is important to assess whether one program might be more effective for individuals with extreme initial difficulties, while another might be more effective for individuals with less extreme initial difficulties. In setting in which time-series data are obtained for each person, this entails examining interactions…

The Individuals With Disabilities Education Act (IDEA) Amendments of 1997 mandate the use of functional behavioral assessment (FBA) and positive behavioral supports and interventions for students with disabilities. Although much progress has been made in our understanding of functional analysis over the past 15 years, the extent to which these…