Every year, around two thousand Huichol families migrate from their homelands in the highlands of northwestern Mexico to the coastal region of Nayarit State, where they are employed on small plantations to pick and thread tobacco leaves. During their four-month stay, they live, work, eat, and sleep in the open air next to the tobacco fields, exposing themselves to an unknown cocktail of pestici...

High-throughput studies have been extensively conducted in the research of complex human diseases. As a representative example, consider gene-expression studies where thousands of genes are profiled at the same time. An important objective of such studies is to rank the diagnostic accuracy of biomarkers (e.g. gene expressions) for predicting outcome variables while properly adjusting for confou...

In studies assessing the effects of a given exposure variable and a specific outcome of interest, confusion may arise from the mistaken impression that the exposure variable is producing the outcome of interest, when in fact the observed effect is due to an existing confounder. However, quantitative techniques are rarely used to determine the potential influence of unmeasured confounders. Sensi...

We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But the estimation results could be distorted if some assumptions are violated. In this letter, we propose a new algorithm for learning causal orders that is robust...

Measurement error in explanatory variables and unmeasured confounders can cause considerable problems in epidemiologic studies. It is well recognized that under certain conditions, nondifferential measurement error in the exposure variable produces bias towards the null. Measurement error in confounders will lead to residual confounding, but this is not a straightforward issue, and it is not cl...

This article discusses the importance, definition, and types of confounders in epidemiology. Methods to identify and address confounding are discussed, as well as their strengths and limitations. The article also describes the difference among confounders, mediators, and effect modifiers.