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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.

no code implementations • 28 Jan 2021 • Tineke Blom, Joris M. Mooij

Furthermore, we give sufficient conditions to test for the presence of perfect adaptation in experimental equilibrium data.

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

Often, 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

We formulate a new causal bandit algorithm that is the first to no longer rely on explicit prior causal knowledge and instead uses the output of causal discovery algorithms.

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

When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results.

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 • 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.

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.

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