arXiv:2607. 21208v1 Announce Type: new Abstract: Pearl famously argues that causal knowledge enables the prediction of intervention effects.
By Kilian Rueckschloss (Eberhard Karls Universitaet Tuebingen), Felix Weitkaemper (German University of Digital Science)
arXiv:2608. 07230v1 Announce Type: new Abstract: Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system.
By Zora Wurm, Kilian R\"uckschlo{\ss}, Felix Weitk\"amper
arXiv:2607. 21233v1 Announce Type: new Abstract: Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual states of causal processes compatible with those logic programs.
By Felix Weitk\"amper
arXiv:2606. 28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
By Malte Luttermann, Tanya Braun, Ralf M\"oller, Marcel Gehrke
arXiv:2607. 26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations.
By Jule Schmidt, Maximilian Weininger, Clemens Dubslaff, David Parker, Nils Jansen
arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.
By Zihao Li, Fabrizio Russo