Joris M. Mooij

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions

Unobserved confounding is one of the main challenges when estimating cau...
03/08/2021 ∙ by Maximilian Ilse, et al. ∙ 16 ∙ share

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Causality and independence in perfectly adapted dynamical systems

Perfect adaptation in a dynamical system is the phenomenon that one or m...
01/28/2021 ∙ by Tineke Blom, et al. ∙ 0 ∙ share

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Robustness of Model Predictions under Extension

Often, mathematical models of the real world are simplified representati...
12/08/2020 ∙ by Tineke Blom, et al. ∙ 0 ∙ share

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A Weaker Faithfulness Assumption based on Triple Interactions

One of the core assumptions in causal discovery is the faithfulness assu...
10/27/2020 ∙ by Alexander Marx, et al. ∙ 0 ∙ share

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Causal Discovery for Causal Bandits utilizing Separating Sets

The Causal Bandit is a variant of the classic Bandit problem where an ag...
09/16/2020 ∙ by Arnoud A. W. M. de Kroon, et al. ∙ 0 ∙ share

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

The performance of constraint-based causal discovery algorithms is promi...
08/17/2020 ∙ by Philip A. Boeken, et al. ∙ 0 ∙ share

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Conditional Independences and Causal Relations implied by Sets of Equations

Real-world systems are often modelled by sets of equations with exogenou...
07/14/2020 ∙ by Tineke Blom, et al. ∙ 0 ∙ share

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

While feedback loops are known to play important roles in many complex s...
05/01/2020 ∙ by Joris M. Mooij, et al. ∙ 0 ∙ share

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Constraint-Based Causal Discovery In The Presence Of Cycles

While feedback loops are known to play important roles in many complex s...
05/01/2020 ∙ by Joris M. Mooij, et al. ∙ 0 ∙ share

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Boosting Local Causal Discovery in High-Dimensional Expression Data

We study how well Local Causal Discovery (LCD), a simple and efficient c...
10/06/2019 ∙ by Philip Versteeg, et al. ∙ 0 ∙ share

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

We prove the main rules of causal calculus (also called do-calculus) for...
01/02/2019 ∙ by Patrick Forré, et al. ∙ 0 ∙ share

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An Upper Bound for Random Measurement Error in Causal Discovery

Causal discovery algorithms infer causal relations from data based on se...
10/18/2018 ∙ by Tineke Blom, et al. ∙ 0 ∙ share

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Algebraic Equivalence of Linear Structural Equation Models

Despite their popularity, many questions about the algebraic constraints...
07/10/2018 ∙ by Thijs van Ommen, et al. ∙ 6 ∙ share

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

We address the problem of causal discovery from data, making use of the ...
07/09/2018 ∙ by Patrick Forré, et al. ∙ 0 ∙ share

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Generalized Strucutral Causal Models

Structural causal models are a popular tool to describe causal relations...
05/16/2018 ∙ by Tineke Blom, et al. ∙ 0 ∙ share

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From Random Differential Equations to Structural Causal Models: the stochastic case

Random Differential Equations provide a natural extension of Ordinary Di...
03/23/2018 ∙ by Stephan Bongers, et al. ∙ 0 ∙ share

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Markov Properties for Graphical Models with Cycles and Latent Variables

We investigate probabilistic graphical models that allow for both cycles...
10/24/2017 ∙ by Patrick Forré, et al. ∙ 0 ∙ share

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Causal Transfer Learning

An important goal in both transfer learning and causal inference is to m...
07/20/2017 ∙ by Sara Magliacane, et al. ∙ 0 ∙ share

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Causal Consistency of Structural Equation Models

Complex systems can be modelled at various levels of detail. Ideally, ca...
07/04/2017 ∙ by Paul K. Rubenstein, et al. ∙ 0 ∙ share

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Joint Causal Inference from Observational and Experimental Datasets

We introduce Joint Causal Inference (JCI), a powerful formulation of cau...
11/30/2016 ∙ by Sara Magliacane, et al. ∙ 0 ∙ share

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Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions

Structural causal models (SCMs), also known as non-parametric structural...
11/18/2016 ∙ by Stephan Bongers, et al. ∙ 0 ∙ share

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Ancestral Causal Inference

Constraint-based causal discovery from limited data is a notoriously dif...
06/22/2016 ∙ by Sara Magliacane, et al. ∙ 0 ∙ share

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Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is ...
12/11/2014 ∙ by Joris M. Mooij, et al. ∙ 0 ∙ share

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

This article contains detailed proofs and additional examples related to...
11/06/2014 ∙ by Tom Claassen, et al. ∙ 0 ∙ share

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Joris M. Mooij

Featured Co-authors

Bernhard Schölkopf

Max Welling

Arthur Gretton

Dominik Janzing

Jonas Peters

Tom Heskes

Patrick Forré

Tom Claassen

Moritz Grosse-Wentrup

Sebastian Weichwald

Paul K. Rubenstein

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions

Causality and independence in perfectly adapted dynamical systems

Robustness of Model Predictions under Extension

A Weaker Faithfulness Assumption based on Triple Interactions

Causal Discovery for Causal Bandits utilizing Separating Sets

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

Conditional Independences and Causal Relations implied by Sets of Equations

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

Constraint-Based Causal Discovery In The Presence Of Cycles

Boosting Local Causal Discovery in High-Dimensional Expression Data

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

An Upper Bound for Random Measurement Error in Causal Discovery

Algebraic Equivalence of Linear Structural Equation Models

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

Generalized Strucutral Causal Models

From Random Differential Equations to Structural Causal Models: the stochastic case

Markov Properties for Graphical Models with Cycles and Latent Variables

Causal Transfer Learning

Causal Consistency of Structural Equation Models

Joint Causal Inference from Observational and Experimental Datasets

Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions

Ancestral Causal Inference

Distinguishing cause from effect using observational data: methods and benchmarks

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