We present an overview of the decision-theoretic framework of statistical causality, which is
well suited for formulating and solving problems of determining the effects of applied causes …

Observational studies in which the effect of a nonrandomized treatment on an outcome of
interest is estimated are common in domains such as labour economics and epidemiology …

When causal effects are to be estimated from observational data, we have to adjust for
confounding. A central aim of covariate selection for causal inference is therefore to …

We present a graphical criterion for covariate adjustment that is sound and complete for four
different classes of causal graphical models: directed acyclic graphs (DAGs), maximal …

We consider the problem of learning about and comparing the consequences of dynamic
treatment strategies on the basis of observational data. We formulate this within a …

We develop a mathematical and interpretative foundation for the enterprise of decision-
theoretic (DT) statistical causality, which is a straightforward way of representing and …

Many legal cases require decisions about causality, responsibility or blame, and these may
be based on statistical data. However, causal inferences from such data are beset by subtle …

The goal of this paper is to integrate the notions of stochastic conditional independence and
variation conditional independence under a more general notion of extended conditional …

Statistical approaches for estimating treatment effectiveness commonly model the endpoint,
or the propensity score, using parametric regressions such as generalised linear models …

Modelling causal relationships has become popular across various disciplines. Most
common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) …