A survey on causal discovery: theory and practice
Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …
especially true when the goal is to model the interplay between different aspects in a causal …
Maximum satisfiabiliy
Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …
[HTML][HTML] Improvements to the implicit hitting set approach to pseudo-Boolean optimization
P Smirnov, J Berg, M Järvisalo - 25th International Conference on …, 2022 - drops.dagstuhl.de
The development of practical approaches to efficiently reasoning over pseudo-Boolean
constraints has recently increasing attention as a natural generalization of Boolean …
constraints has recently increasing attention as a natural generalization of Boolean …
[PDF][PDF] Pseudo-boolean optimization by implicit hitting sets
P Smirnov, J Berg, M Järvisalo - … on Principles and …, 2021 - researchportal.helsinki.fi
Recent developments in applying and extending Boolean satisfiability (SAT) based
techniques have resulted in new types of approaches to pseudo-Boolean optimization …
techniques have resulted in new types of approaches to pseudo-Boolean optimization …
[HTML][HTML] Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach
Understanding causal relationships is a central challenge in many research endeavours.
Recent research has shown the importance of accounting for feedback (cycles) and latent …
Recent research has shown the importance of accounting for feedback (cycles) and latent …
[HTML][HTML] Quantum algorithm for variant maximum satisfiability
A Alasow, P Jin, M Perkowski - Entropy, 2022 - mdpi.com
In this paper, we proposed a novel quantum algorithm for the maximum satisfiability
problem. Satisfiability (SAT) is to find the set of assignment values of input variables for the …
problem. Satisfiability (SAT) is to find the set of assignment values of input variables for the …
Learning optimal causal graphs with exact search
K Rantanen, A Hyttinen… - … on Probabilistic Graphical …, 2018 - proceedings.mlr.press
Discovering graphical models over very general model spaces with high accuracy requires
optimally combining conflicting (in) dependence constraints in sample data, and thus results …
optimally combining conflicting (in) dependence constraints in sample data, and thus results …
[PDF][PDF] A comprehensive survey of the actual causality literature
KR Kueffner - 2021 - scholar.archive.org
The study of causality has recently gained traction in computer science. Formally capturing
causal reasoning would allow computers to answer “Why”-questions and would result in …
causal reasoning would allow computers to answer “Why”-questions and would result in …
Learning optimal cyclic causal graphs from interventional data
K Rantanen, A Hyttinen… - … on Probabilistic Graphical …, 2020 - proceedings.mlr.press
We consider causal discovery in a very general setting involving non-linearities, cycles and
several experimental datasets in which only a subset of variables are recorded. Recent …
several experimental datasets in which only a subset of variables are recorded. Recent …
Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity
F Eberhardt, N Kaynar, A Siddiq - Management Science, 2024 - pubsonline.informs.org
We propose a new optimization-based method for learning causal structures from
observational data, a process known as causal discovery. Our method takes as input …
observational data, a process known as causal discovery. Our method takes as input …