Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the re...

| Designing distributed multimedia applications raises temporal and spatial synchronization issues related to processing, transport, storage, retrieval and presentation of data, sound, still images and video. Within this framework , the paper aims to deene a general-purpose multime-dia synchronization mechanism, known as the conditional delivery mechanism capable of addressing both intra-and in...

Constraint-based causal discovery algorithms, such as the PC algorithm, rely on conditional independence tests and are otherwise independent of the actual distribution of the data. In case of continuous variables, the most popular conditional independence test used in practice is the partial correlation test, applicable to variables that are multivariate Normal. Many researchers assume multivar...

We use causal forests to evaluate the heterogeneous treatment effects (TEs) of repeated behavioral nudges toward household energy conservation. The average response is a monthly electricity reduction 9 kilowatt-hours (kWh), but full distribution responses ranges from -40 +10 kWh. Households learn reduce more over time, conditional on having responded in year one. Pre-treatment consumption and h...

Many public health interventions provide benefits that extend beyond their direct recipients and impact people in close physical or social proximity who did not directly receive the intervention themselves. A classic example of this phenomenon is the herd protection provided by many vaccines. If these 'spillover effects' (i.e. 'herd effects') are present in the same direction as the effects on ...

A causal graph G(V, E) specifies causal relationships among the random variables representing the vertices of the graph V . The relationships are specified by the directed edges E ; an edge Vi ! Vj implies that Vi 2 V is a direct parental cause for the effect Vj 2 V . With some abuse of notation, we will denote the random variable associated with a node V 2 V by V itself. We will denote the par...

Simple belief-revision tasks were defined by a giving subjects a conditional premise, (p—>q), a categorical premise, (p, for a modus-ponens belief-set, or ~q, for a modus tollens belief-set), and the associated inference (q or ~p, respectively). "New" information contradicted the initial inference (~ q or p, respectively). Subjects indicated their degree of belief in the conditional premise and...

We extend the 0-approximation of sensing actions and incomplete information in (Son and Baral 2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with respect to this approximation is NP-complete. We then present an answer set programming based conditional planner, called ASCP, ...

This paper explores the discrete Dynamic Causal Modeling (DDCM) and its relationship with Directed Information (DI). We prove the conditional equivalence between DDCM and DI in characterizing the causal relationship between two brain regions. The theoretical results are demonstrated using fMRI data obtained under both resting state and stimulus based state. Our numerical analysis is consistent ...

A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines (1993; cf. Pearl, 2000). The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do ope...