Causal models of illness vary extensively across socio-cultural groups. The current paper describes two studies that were designed to explore the role of universal domain-specific causal knowledge in causal models of illness. The first study compares illness causal models in three American groups: registered nurses, energy healers, and college undergraduates. The second study examines illness c...

Summary We study identification and estimation of causal effects in settings with panel data. Traditionally, researchers follow model-based strategies relying on assumptions governing the relation between potential outcomes observed unobserved confounders. focus a different, complementary approach to identification, where are made about connection treatment assignment Such common cross-section ...

Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all machine learning based methods, they are as good training data, and can capture unwanted biases. While there tools that help understand whether such biases exist, do not distinguish between correlation causation, might be ill-suited for text-based models reasoning abo...

This article proposes that learning of categories based on cause-effect relations is guided by causal models. In addition to incorporating domain-specific knowledge, causal models can be based on knowledge of such general structural properties as the direction of the causal arrow and the variability of causal variables. Five experiments tested the influence of commoncause models and common-effe...

We offer evidence that people construe mathematical relations as causal. The studies show that people can select the causal versions of equations, and that their selections predict both what they consider most understandable and their expectations of change. When asked to write down equations, people have a strong preference for the version that matches their causal model. Causal Models Frame 3...

BACKGROUND/AIMS To illustrate the utility of causal models for research in genetic epidemiology and statistical genetics. Causal models are increasingly applied in risk factor epidemiology, economics, and health policy, but seldom used in statistical genetics or genetic epidemiology. Unlike the statistical models usually used in genetic epidemiology, causal models are explicitly formulated in t...

In the present study, records on 22872 first-parity Holsteins collected from 131 herds by the Animal Breeding and Improvement Center of Iran from 1995 to 2014 were considered to estimate genetic and phenotypic relationships between calving difficulty (CD) and the lactation curve traits, including initial milk yield (Ap), ascending (Bp) and descending (Cp) slope of the lactation curves, peak mil...

1 Theory and Experience . . . . . . . . . . . . . . . . . . . . . . . 1 2 Causal analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1 Causal models . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Theory of causal regularities . . . . . . . . . . . . . . . . . . . 7 2.3 Principles of causal reasoning . . . . . . . . . . . . . . . . . . 7 2.3.1 Method of Difference . . ....