In recent papers, Lee & Holyoak (2007, 2008a, 2008b) argue that extant models of analogy fail to explain how people draw inferences from causal analogies. In the current research, we argue that structure-mapping theory sufficiently explains the analogical inferences drawn from these causal analogies, and that, contrary to L&H‘s claims, the effect inference can indeed be evaluated by a post-anal...

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a ...

Abstract Objective: There is some evidence that causal beliefs are related with adherence behaviors. The aim of the present study was to explore the relationship between illness causal beliefs, demographic factors and medication adherence among a group of patients with type 2 diabetes. Materials and Methods: Ninety-three patients with type 2 diabetes participated in this study using convenien...

In clinical trials with significant noncompliance the standard intention-to-treat analyses sometimes mislead. Rubin’s causal model provides an alternative method of analysis that can shed extra light on clinical trial data. Formulating the Rubin Causal Model as a graphical model facilitates model communication and computation.

Three experiments investigated the impact of delay on human causal learning. We present a new paradigm based on the presentation of continuous event streams, and use it to test two hypotheses drawn from associative learning theories of causal inference. Unlike free-operant procedures traditionally used to study temporal aspects of causal learning (Shanks, Pearson, & Dickinson, 1989; Shanks & Di...

This paper treats the calculation of the effect of an intervention (also called causal effect) on a variable from a combination of observational data and some theoretical assumptions. Observational data implies that the modeler has no way to do experiments to assess the effect of one variable on some others, instead he possesses data collected by observing variables in the domain he is investig...

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In our data-­‐rich world, key medical decisions, ranging from a regulator's decision to curtail a drug to patient-­‐specific treatment choices require optimal consideration of myriad inputs. Statistical/epidemiological methods that can harness real-­‐world medical data in useful ways do exis...

counterfactual analysis is concerned with explaining events that have not happened. counterfactuals are mental experiments through which one can reconstruct hypothetical versions of the history in one’s mind; these versions are relatively different from the real history, but provide one with the opportunity to test historical hypotheses against the available evidence. historicist researchers in...

cerato-ulmin is the most important toxin of fungal causal agents of dutch elm disease and its content is one of the most important factors to distinguish two species ophiostoma ulmi and o. novo-ulmi. with attention to the ophiostoma novo-ulmi is more pathogenicity than o. ulmi, this research was carried out to discuss the role of cerato-ulmin on pathogenicity of causal agent using measurement o...