In epidemiological research, the causal effect of a modifiable phenotype or exposure on a
disease is often of public health interest. Randomized controlled trials to investigate this …

Interest in the skew-normal and related families of distributions has grown enormously over
recent years, as theory has advanced, challenges of data have grown, and computational …

Observational epidemiological studies suffer from many potential biases, from confounding
and from reverse causation, and this limits their ability to robustly identify causal …

This paper highlights several areas where graphical techniques can be harnessed to
address the problem of measurement errors in causal inference. In particular, it discusses …

Ordered sequences of univariate or multivariate regressions provide statistical models for
analysing data from randomized, possibly sequential interventions, from cohort or multi …

The supplement contains additional material such as further elements of proofs, a hardness
result for checking the LF-HTC without a bound on the cardinality of searched sets of latent …

Assume that cause-effect relationships between variables can be described as a directed
acyclic graph and the corresponding linear structural equation model. We consider the …

Covariance matrices which can be arranged in tridiagonal form are called covariance
chains. They are used to clarify some issues of parameter equivalence and of independence …

We study parameter identifiability of directed Gaussian graphical models with one latent
variable. In the scenario we consider, the latent variable is a confounder that forms a source …

Undetected confounding may severely distort the effect of an explanatory variable on a
response variable, as defined by a stepwise data-generating process. The best known type …