Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness

(2015) Sander Greenland et al.   EUROPEAN JOURNAL OF EPIDEMIOLOGY

Serum bilirubin and the risk of hypertension

(2014) Lina Wang et al.   INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Restoring Causal Analysis to Structural Equation ModelingReview ofCausality: Models, Reasoning, and Inference(2nd Edition), by Judea Pearl

(2014) Stephen G. West et al.   STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL

The SSQ model of schizophrenic prodromal unfolding revised: An analysis of its causal chains based on the language of directed graphs

(2013) D. van Kampen   EUROPEAN PSYCHIATRY

DAGitty

(2011) Johannes Textor et al.   EPIDEMIOLOGY

Testing conditional independence using maximal nonlinear conditional correlation

(2010) Tzee-Ming Huang   ANNALS OF STATISTICS

Reducing bias through directed acyclic graphs

(2008) Ian Shrier et al.   BMC Medical Research Methodology

Letter to the Editor

(2007) Ian Shrier   STATISTICS IN MEDICINE