Many empirical sciences, including the social sciences and life sciences, aim to study causal relationships. Researchers in these fields need computational methods for analyzing observed data and identifying causal structures among a set of variables. Such computational methods enable researchers to draw conclusions on the basis of both their assumptions and the observed data. Moreover, these m...

We present an algorithmic framework for learning local causal structure around target variables of interest in the form of direct causes/effects and Markov blankets applicable to very large data sets with relatively small samples. The selected feature sets can be used for causal discovery and classification. The framework (Generalized Local Learning, or GLL) can be instantiated in numerous ways...

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...

Many areas of political science focus on causal questions. Evidence from statistical analyses are often used to make the case for causal relationships. While statistical evidence can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on statistica...

BACKGROUND Causal estimation methods are increasingly being used to investigate functional brain networks in fMRI, but there are continuing concerns about the validity of these methods. NEW METHOD Multivariate dynamical systems (MDS) is a state-space method for estimating dynamic causal interactions in fMRI data. Here we validate MDS using benchmark simulations as well as simulations from a m...