This paper considers an extension of the linear non-Gaussian acyclic model (LiNGAM) that determines causal order among variables from a dataset when are expressed by set equations, including noise. In particular, we assume binary. The existing LiNGAM assumes no confounding is present, which restrictive in practice. Based on concept independent component analysis (ICA), this proposes extended fr...

When a difference in an outcome (eg, pain) between exposures (eg, treatment groups) is observed, one needs to consider whether the effect is truly because of exposure or if alternate explanations are possible. As we discussed in the previous issue of EBSJ, to evaluate the validity of a research study, factors that might distort the true association and/or influence its interpretation need to be...

In a presentation of various methods for assessing the sensitivity of regression results to unmeasured confounding, Lin, Psaty, and Kronmal (1998, Biometrics54, 948-963) use a conditional independence assumption to derive algebraic relationships between the true exposure effect and the apparent exposure effect in a reduced model that does not control for the unmeasured confounding variable. How...

The paper considers the properties of and relations between confounding and effect modification from the perspective of causal inference and with a distinction drawn as to how each of these two epidemiologic concepts can be defined both with respect to a distribution of potential outcomes or with respect to a specific effect measure. Both concepts are conditional on other covariates but the for...