Context: The aim of statistical causal inference (SCI) methods is to estimate causal effects
from observational data (ie, when randomized controlled trials are not possible). In this …

Detecting and understanding reasons for defects and inadvertent behavior in software is
challenging due to their increasing complexity. In configurable software systems, the …

Fully formal verification of perception models is likely to remain challenging in the
foreseeable future, and yet these models are being integrated into safety-critical control …

As reinforcement learning methods increasingly amass accomplishments, the need for
comprehending their solutions becomes more crucial. Most explainable reinforcement …

Counterfactual reasoning is an approach to infer what causes an observed effect by
analyzing the hypothetical scenarios where a suspected cause is not present. The seminal …

Modern cyber-physical systems would often fall victim to unanticipated anomalies. Humans
are still required in many operations to troubleshoot and respond to such anomalies, such …

Hyperproperties relate multiple computation traces to each other. Model checkers for
hyperproperties thus return, in case a system model violates the specification, a set of traces …

Temporal causality describes what concrete input behavior is responsible for some
observed output behavior on a trace of a reactive system, and can be used to, eg, generate …

The surge in popularity of Large Language Models (LLMs) has opened doors for new
approaches to the creation of interactive agents. However, managing the temporal behavior …

Principled accountability in the aftermath of harms is essential to the trustworthy design and
governance of algorithmic decision making. Legal theory offers a paramount method for …