281 citations · 1.2k across the 37 of their papers we have counts for
37 papers
Causal Reasoning with Bipartite Graphical Causal Models
Joris M. Mooij
Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they cannot adequately represent all real-world…
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…
Correcting for Selection Bias and Missing Response in Regression using Privileged Information
Philip Boeken, Noud de Kroon, Mathijs de Jong +2
When estimating a regression model, we might have data where some labels are missing, or our data might be biased by a selection mechanism. When the response or selection mechanism…
Local Constraint-Based Causal Discovery under Selection Bias
Philip Versteeg, Cheng Zhang, Joris M. Mooij
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present. While the seminal FCI algorithm is sound…
Causality and independence in perfectly adapted dynamical systems
Tineke Blom, Joris M. Mooij
Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert t…
Robustness of Model Predictions under Extension
Tineke Blom, Joris M. Mooij
Mathematical models of the real world are simplified representations of complex systems. A caveat to using mathematical models is that predicted causal effects and conditional inde…