This article reviews the recent literature on algorithmic fairness, with a particular emphasis
on credit scoring. We discuss human versus machine bias, bias measurement, group versus …

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big
data generated by IT Operations processes, particularly in cloud infrastructures, to provide …

We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages
graphical causal models. Unlike existing causality libraries, which mainly focus on effect …

We study counterfactual identifiability in causal models with bijective generation
mechanisms (BGM), a class that generalizes several widely-used causal models in the …

Complex diseases are caused by a multitude of factors that may differ between patients
even within the same diagnostic category. A few underlying root causes may nevertheless …

Counterfactual reasoning—envisioning hypothetical scenarios, or possible worlds, where
some circumstances are different from what (f) actually occurred (counter-to-fact)—is …

This paper presents an approach for identifying the root causes of collective anomalies
given observational time series and an acyclic summary causal graph which depicts an …

Counterfactual inference aims to answer retrospective" what if" questions and thus belongs
to the most fine-grained type of inference in Pearl's causality ladder. Existing methods for …

Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted
outcome. In prior work, we defined sample-specific root causes of disease using exogenous …

Objective: Root causes of disease intuitively correspond to root vertices of a causal model
that increase the likelihood of a diagnosis. This description of a root cause nevertheless …