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 …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

Covariate adjustment is a commonly used method for total causal effect estimation. In recent
years, graphical criteria have been developed to identify all valid adjustment sets, that is, all …

We consider estimation of a total causal effect from observational data via covariate
adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting …

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 …

Fair machine learning aims to avoid treating individuals or sub-populations unfavourably
based on\textit {sensitive attributes}, such as gender and race. Those methods in fair …

The ability to understand causality from data is one of the major milestones of human-level
intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships …

Causal Discovery (CD) is the process of identifying the cause-effect relationships among the
variables from data. Over the years, several methods have been developed primarily based …

Even when the causal graph underlying our data is unknown, we can use observational
data to narrow down the possible values that an average treatment effect (ATE) can take by …

We introduce the problem of active causal structure learning with advice. In the typical well-
studied setting, the learning algorithm is given the essential graph for the observational …