It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal ...

Time and frequency domain convergence properties of causal and Current Iteration Tracking Error (CITE) discrete time Iterative Learning Control (ILC) algorithms are discussed. Considering necessary and sufficient convergence conditions basic matrix properties can be utilized to show that causal as well as CITE ILC algorithms converge to zero error in only very restrictive special cases. The fre...

The causal feedback implied by urban neighborhood conditions that shape human health experiences, that in turn shape neighborhood conditions through a complex causal web, raises a challenge for traditional epidemiological causal analyses. This article introduces the loop analysis method, and builds off of a core loop model linking neighborhood property vacancy rate, resident depressive symptoms...

PURPOSE Causal effects in epidemiology are almost invariably studied by considering disease incidence even when prevalence data are used to estimate the causal effect. For example, if certain conditions are met, a prevalence odds ratio can provide a valid estimate of an incidence rate ratio. Our purpose and main result are conditions that assure causal effects on prevalence can be estimated in ...

This study examined the effects of scripting learners’ use of two types of representational tools (i.e., causal and simulation) on their online collaborative problem-solving. Scripting sequenced the phase-related part-task demands and made them explicit. This entailed (1) defining the problem and proposing multiple solutions (i.e., problem-solution) and (2) evaluating solutions and coming to a ...

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After t...

Time series are often subject to conflicting forces; we refer to these as complex time series. This paper uses of causal forces in order to decompose complex series. In particular, we hypothesized three conditions to be important for effectively decomposing a time series by causal forces: 1) the forecaster has domain knowledge that can not be applied directly to the target series, 2) the domain...