We consider the task of inferring causal relations in brain imaging data with latent confounders. Using a priori knowledge that randomized experimental conditions cannot be effects of brain activity, we derive statistical conditions that are sufficient for establishing a causal relation between two neural processes, even in the presence of latent confounders. We provide an algorithm to test the...

The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must hold. We review the definition of an instrumental variable, describe the conditions required to ob...

This supplement describes how counterfactual theory is used to define causal effects and the conditions in which observed data can be used to estimate counterfactual-based causal effects. Basic definitions and language used in causal graph theory are then presented. The graphical separation rules linking the causal assumptions encoded in a diagram to the statistical relations implied by the cau...

Background: The marital adjustment has a positive effect on the physical and mental health of family members. The purpose of this study is to explain the process of marital adjustment in Iranian women. Methods: This research is based on qualitative data analysis using the grounded theory method. Theoretical and purposeful sampling was used for data collection. Sampling continued until theoreti...

A unifying framework for the study of causal relations is presented. The causal relations are regarded as subset of M × M and the role of the corresponding antisymmetry conditions in the construction of the causal ladder is stressed. The causal hierarchy of spacetime is built from chronology up to K-causality and new characterizations of the distinction and strong causality properties are obtai...

Therefore, if randomization is successful, a simple comparison among the observed potential outcomes is the causal effect. In other words, randomization helps simple two-sample test report causal inference results. In causal DAG, successful randomization means that there are no arrows from confounders to the exposure variable. This means that regardless of conditions of confounders, the exposur...

Quantum causal histories are defined to be causal sets with Hilbert spaces attached to each event and local unitary evolution operators. The reflexivity, antisymmetry, and transitivity properties of a causal set are preserved in the quantum history as conditions on the evolution operators. A quantum causal history in which transitivity holds can be treated as “directed” topological quantum fiel...

We do things in the world by exploiting our knowledge of what causes what. But in trying to reason formally about causality, there is a difficulty: to reason with certainty we need complete knowledge of all the relevant events and circumstances, whereas in everyday reasoning tasks we need a more serviceable but looser notion that does not make such demands on our knowledge. In this work the not...