background: there are several conflicting conceptual models to explain social determinants of health (sdh) as responsible for most health inequalities. this study aimed to present these models in historical perspective and provide main component of sdh models as an ses indicators. methods: this was a narrative study using international databases to retrieve literature dealing with conceptual mo...

conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  as  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itsel...

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The softw...

Throughout our lives, we are constantly faced with a variety of causal reasoning problems. A challenge for cognitive modelers is developing a comprehensive framework for modeling causal reasoning across different types of tasks and levels of causal complexity. Causal graphical models (CGMs), based on Bayes’ calculus, have perhaps been the most successful at explaining and predicting judgments o...

Recently, it has been demonstrated that graphical models promise some potential for expressing causal concepts, see for example Pearl (2000), Lauritzen (2001), or Dawid (2002). The causal interpretation is most direct in models based on directed acyclic graphs, whereas causal interpretation for chain graph models generally is more subtle and complex (Lauritzen and Richardson 2002). In the artic...

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we propose an application of an extended class of causal independence models, causal independence models based on the symmetric Boolean function, for classification. We present an EM algorithm to learn the paramet...

Nonlinear regression with measurement error is important for estimation from microeconomic data. One approach to identification and estimation is a causal model, where the unobserved true variable is predicted by observable variables. This paper is about estimation of such a model using simulated moments and a flexible disturbance distribution. An estimator of the asymptotic variance is given f...

Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data gener...

knowledge can come in various forms, and it may form complex hierarchies. The most abstract form of knowledge may be called causal principles, which probably include the assumption that causes precede their effects, that nothing happens without a cause, and that causes generate their effect unless prevented by an inhibitory factor (Audi 1995). A fundamental assumption might also be that a manip...

Models for numerical simulations should be described in a coherent style. They are expected to have consistencies at the causal dependency level. However, System Dynamics causal loop diagrams can have inconsistencies. This diagram style’s arrows, concerning flow and stock relationships, can have the opposite direction of stock flow diagrams which can numerically simulate models. These inconsist...