281 citations · 1.2k across the 37 of their papers we have counts for
8 papers · 1 filter
A Weaker Faithfulness Assumption based on Triple Interactions
Alexander Marx, Arthur Gretton, Joris M. Mooij
One of the core assumptions in causal discovery is the faithfulness assumption, i.e., assuming that independencies found in the data are due to separations in the true causal graph…
Boosting Local Causal Discovery in High-Dimensional Expression Data
Philip Versteeg, Joris M. Mooij
We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expr…
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
Patrick Forré, Joris M. Mooij
We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear…
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
Patrick Forré, Joris M. Mooij
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, lat…
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris Mooij +3
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for pol…
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…