Search Results for author: Joris M. Mooij

Found 29 papers, 6 papers with code

Combining Interventional and Observational Data Using Causal Reductions

1 code implementation8 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.

Causal Inference

Causality and independence in perfectly adapted dynamical systems

no code implementations28 Jan 2021 Tineke Blom, Joris M. Mooij

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

Causal Discovery

Robustness of Model Predictions under Extension

no code implementations8 Dec 2020 Tineke Blom, Joris M. Mooij

Often, mathematical models of the real world are simplified representations of complex systems.

A Weaker Faithfulness Assumption based on Triple Interactions

no code implementations27 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.

Causal Discovery

Causal Discovery for Causal Bandits utilizing Separating Sets

no code implementations16 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.

Causal Discovery Decision Making

A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery

1 code implementation17 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

Conditional independences and causal relations implied by sets of equations

no code implementations14 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.

Causal Discovery

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

no code implementations1 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.

Causal Discovery Causal Inference

Boosting Local Causal Discovery in High-Dimensional Expression Data

no code implementations6 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.

Causal Discovery

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

no code implementations2 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.

Selection bias

An Upper Bound for Random Measurement Error in Causal Discovery

no code implementations18 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.

Causal Discovery

Algebraic Equivalence of Linear Structural Equation Models

1 code implementation10 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.

Causal Discovery Model Selection

Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders

1 code implementation9 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.

Causal Discovery

Beyond Structural Causal Models: Causal Constraints Models

no code implementations16 May 2018 Tineke Blom, Stephan Bongers, Joris M. Mooij

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

Causal Modeling of Dynamical Systems

no code implementations23 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.

Markov Properties for Graphical Models with Cycles and Latent Variables

no code implementations24 Oct 2017 Patrick Forré, Joris M. Mooij

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

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

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.

Causal Inference Domain Adaptation

Joint Causal Inference from Multiple Contexts

no code implementations30 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.

Causal Discovery Causal Inference

Foundations of Structural Causal Models with Cycles and Latent Variables

no code implementations18 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.

From Deterministic ODEs to Dynamic Structural Causal Models

no code implementations29 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.

Ancestral Causal Inference

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.

Causal Discovery Causal Inference

Distinguishing cause from effect using observational data: methods and benchmarks

no code implementations11 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.

Causal Discovery Causal Inference +1

Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)

no code implementations6 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 $N$.

Model Discovery Selection bias

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

no code implementations30 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).

On Causal Discovery with Cyclic Additive Noise Models

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.

Causal Discovery

Probabilistic latent variable models for distinguishing between cause and effect

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).

Model Selection

Bounds on marginal probability distributions

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.

Medical Diagnosis

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