90 citations · 387 across the 16 of their papers we have counts for
17 papers
Establishing Markov Equivalence in Cyclic Directed Graphs
Tom Claassen, Joris M. Mooij
We present a new, efficient procedure to establish Markov equivalence between directed graphs that may or may not contain cycles under the \textit{d}-separation criterion. It is ba…
Towards a Benchmark for Scientific Understanding in Humans and Machines
Kristian Gonzalez Barman, Sascha Caron, Tom Claassen +1
Scientific understanding is a fundamental goal of science, allowing us to explain the world. There is currently no good way to measure the scientific understanding of agents, wheth…
Spectral Ranking of Causal Influence in Complex Systems
Errol Zalmijn, Tom Heskes, Tom Claassen
Like natural complex systems such as the Earth's climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders…
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an ex…
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal in…
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur +1
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a m…
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
Joris M. Mooij, Tom Claassen
While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typica…
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that…
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen +1
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that…
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen +3
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test…
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canon…
Joint Causal Inference from Multiple Contexts
Joris M. Mooij, Sara Magliacane, Tom Claassen
The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations bet…
Ancestral Causal Inference
Sara Magliacane, Tom Claassen, Joris M. Mooij
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the r…
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
Tom Claassen, Joris M. Mooij, Tom Heskes
This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a…
Learning Sparse Causal Models is not NP-hard
Tom Claassen, Joris Mooij, Tom Heskes
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obta…
A Bayesian Approach to Constraint Based Causal Inference
Tom Claassen, Tom Heskes
We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical gu…
A Logical Characterization of Constraint-Based Causal Discovery
Tom Claassen, Tom Heskes
We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about ca…