In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

Causal inference in psychological research is typically hampered by unobserved
confounding. A copula-based method can be used to statistically control for this problem …

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 …

The classical causal relations between a set of variables, some observed and some latent,
can induce both equality constraints (typically conditional independencies) as well as …

Статья исследует инфологические модели как особый вид информационных моделей.
Инфологические модели являются одними из наименее исследованных моделей в …

One common task in many data sciences applications is to answer questions about the
effect of new interventions, like:what would happen to $ Y $ if we make $ X $ equal to $ x …

Causal effect identification is a fundamental task in artificial intelligence. A most ideal
scenario for causal effect identification is that there is a directed acyclic graph as a prior …

Статья исследует информационные модели как метод познания. Описаны методы
построения информационных моделей. Описаны три группы моделей по степени …

Identifying the effects of new interventions from data is a significant challenge found across a
wide range of the empirical sciences. A well-known strategy for identifying such effects is …

The problem of selecting optimal backdoor adjustment sets to estimate causal effects in
graphical models with hidden and conditioned variables is addressed. Previous work has …