317 citations · 496 across the 18 of their papers we have counts for
18 papers
Causal Graphs, Markov Properties and Do-calculus for Stochastic Differential Equations
Philip Boeken, Joris M. Mooij
Stochastic differential equations (SDEs) are widely used to model continuous-time dynamical systems, but graphical causal models for them are not yet well-understood. We consider s…
Complete Causal Identification from Ancestral Graphs under Selection Bias
Leihao Chen, Joris M. Mooij
Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlyi…
Testing Partially-Identifiable Causal Queries Using Ternary Tests
Sourbh Bhadane, Joris M. Mooij, Philip Boeken +1
We consider hypothesis testing of binary causal queries using observational data. Since the mapping of causal models to the observational distribution that they induce is not one-t…
Topological Criteria for Hypothesis Testing with Finite-Precision Measurements
Philip Boeken, Eduardo Skapinakis, Konstantin Genin +1
We establish topological necessary and sufficient conditions under which a pair of statistical hypotheses can be consistently distinguished when i.i.d. observations are recorded on…
Can we detect treatment effect waning from time-to-event data?
Eni Musta, Joris Mooij
Understanding how the causal effect of a treatment evolves over time, including the potential for waning, is important for informed decisions on treatment discontinuation or repeti…
Conditional Forecasts and Proper Scoring Rules for Reliable and Accurate Performative Predictions
Philip Boeken, Onno Zoeter, Joris M. Mooij
Performative predictions are forecasts which influence the outcomes they aim to predict, undermining the existence of correct forecasts and standard methods of elicitation and esti…
-Maximal Ancestral Graphs
Binghua Yao, Joris M. Mooij
Maximal Ancestral Graphs (MAGs) provide an abstract representation of Directed Acyclic Graphs (DAGs) with latent (selection) variables. These graphical objects encode information a…
Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses
Sourbh Bhadane, Joris M. Mooij, Philip Boeken +1
Reasoning about fairness through correlation-based notions is rife with pitfalls. The 1973 University of California, Berkeley graduate school admissions case from Bickel et. al. (1…
Are Bayesian networks typically faithful?
Philip Boeken, Patrick Forré, Joris M. Mooij
Faithfulness is a common assumption in causal inference, often motivated by the fact that the faithful parameters of linear Gaussian and discrete Bayesian networks are typical, and…
Robust Multi-view Co-expression Network Inference
Teodora Pandeva, Martijs Jonker, Leendert Hamoen +2
Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many c…
Dynamic Structural Causal Models
Philip Boeken, Joris M. Mooij
We study a specific type of SCM, called a Dynamic Structural Causal Model (DSCM), whose endogenous variables represent functions of time, which is possibly cyclic and allows for la…
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift
Philip Boeken, Onno Zoeter, Joris M. Mooij
When predicting a target variable from features , the prediction can be performative: an agent might act on this prediction, affecting the value of that we eve…
Foundations of Structural Causal Models with Latent Selection
Leihao Chen, Onno Zoeter, Joris M. Mooij
Three distinct phenomena complicate statistical causal analysis: latent common causes, causal cycles, and latent selection. Foundational works on Structural Causal Models (SCMs), e…
Combining Interventional and Observational Data Using Causal Reductions
Maximilian Ilse, Patrick Forré, Max Welling +1
Unobserved confounding is one of the main challenges when estimating causal effects. We propose a causal reduction method that, given a causal model, replaces an arbitrary number o…
Causal Consistency of Structural Equation Models
Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers +4
Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their p…
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…
Distinguishing cause from effect using observational data: methods and benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing +2
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide w…
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…