no code implementations • 21 Oct 2024 • Philip Boeken, Patrick Forré, Joris M. Mooij

We show that for a given DAG $G$, among all observational distributions of Bayesian networks over $G$ with arbitrary outcome spaces, the faithful distributions are `typical': they constitute a dense, open set with respect to the total variation metric.

no code implementations • 3 Jun 2024 • 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 latent confounding.

no code implementations • 1 Mar 2024 • Philip Boeken, Onno Zoeter, Joris M. Mooij

In this work, we propose to model the deployment of a DSS as causal domain shift and provide novel cross-domain identification results for the conditional expectation $E[Y | X]$, allowing for pre- and post-hoc assessment of the deployment of the DSS, and for retraining of a model that assesses the risk under a baseline policy where the DSS is not deployed.

no code implementations • 12 Jan 2024 • Leihao Chen, Onno Zoeter, Joris M. Mooij

Selection bias is ubiquitous in real-world data, and can lead to misleading results if not dealt with properly.

1 code implementation • 1 Sep 2023 • 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.

1 code implementation • 29 Mar 2023 • Philip Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter

We conclude that repeated regression can appropriately correct for bias, and can have considerable advantage over weighted regression, especially when extrapolating to regions of the feature space where response is never observed.

1 code implementation • 3 Mar 2022 • 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.

1 code implementation • 8 Mar 2021 • Maximilian Ilse, Patrick Forré, Max Welling, Joris M. Mooij

Second, for continuous variables and assuming a linear-Gaussian model, we derive equality constraints for the parameters of the observational and interventional distributions.

1 code implementation • 28 Jan 2021 • 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 to their original value as the system converges to equilibrium.

no code implementations • 8 Dec 2020 • Tineke Blom, Joris M. Mooij

Mathematical models of the real world are simplified representations of complex systems.

no code implementations • 27 Oct 2020 • 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.

no code implementations • 16 Sep 2020 • Arnoud A. W. M. de Kroon, Danielle Belgrave, Joris M. Mooij

Methods proposed for this problem thus far in the literature rely on exact prior knowledge of the full causal graph.

1 code implementation • 17 Aug 2020 • Philip A. Boeken, Joris M. Mooij

In these settings, conditional independence testing with $X$ or $Y$ binary (and the other continuous) is paramount to the performance of the causal discovery algorithm.

Statistics Theory Statistics Theory

no code implementations • 14 Jul 2020 • Tineke Blom, Mirthe M. van Diepen, Joris M. Mooij

Real-world complex systems are often modelled by sets of equations with endogenous and exogenous variables.

no code implementations • 1 May 2020 • 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 typically assumed to be acyclic from the outset.

no code implementations • 6 Oct 2019 • 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 expression data.

no code implementations • 2 Jan 2019 • 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 structural causal models that allow for cycles, latent confounders and arbitrary probability distributions.

no code implementations • 18 Oct 2018 • Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij

Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error.

1 code implementation • 10 Jul 2018 • Thijs van Ommen, Joris M. Mooij

Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems.

1 code implementation • 9 Jul 2018 • 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, latent confounders and non-linearities.

no code implementations • 16 May 2018 • Tineke Blom, Stephan Bongers, Joris M. Mooij

Structural Causal Models (SCMs) provide a popular causal modeling framework.

no code implementations • 23 Mar 2018 • Stephan Bongers, Tineke Blom, Joris M. Mooij

We introduce the formal framework of structural dynamical causal models (SDCMs) that explicates the causal semantics of the system's components as part of the model.

no code implementations • 24 Oct 2017 • Patrick Forré, Joris M. Mooij

We investigate probabilistic graphical models that allow for both cycles and latent variables.

1 code implementation • NeurIPS 2018 • Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij

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) domain(s) differ.

no code implementations • 4 Jul 2017 • Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

Complex systems can be modelled at various levels of detail.

no code implementations • 30 Nov 2016 • Joris M. Mooij, Sara Magliacane, Tom Claassen

We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting.

no code implementations • 18 Nov 2016 • Stephan Bongers, Patrick Forré, Jonas Peters, Joris M. Mooij

In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles.

no code implementations • 29 Aug 2016 • Paul K. Rubenstein, Stephan Bongers, Bernhard Schoelkopf, Joris M. Mooij

Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.

1 code implementation • NeurIPS 2016 • 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.

no code implementations • 11 Dec 2014 • Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data.

no code implementations • 6 Nov 2014 • Tom Claassen, Joris M. Mooij, Tom Heskes

The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.

no code implementations • 30 Apr 2013 • Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).

no code implementations • NeurIPS 2011 • Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise.

no code implementations • NeurIPS 2010 • Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive).

no code implementations • NeurIPS 2008 • Joris M. Mooij, Hilbert J. Kappen

We propose a novel bound on single-variable marginal probability distributions in factor graphs with discrete variables.

no code implementations • NeurIPS 2008 • Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf

The discovery of causal relationships between a set of observed variables is a fundamental problem in science.

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