DeepAI

Joris M. Mooij

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University of Amsterdam

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∙ 03/08/2021

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

Unobserved confounding is one of the main challenges when estimating cau...

Maximilian Ilse, et al. ∙

∙ 01/28/2021

Causality and independence in perfectly adapted dynamical systems

Perfect adaptation in a dynamical system is the phenomenon that one or m...

Tineke Blom, et al. ∙

∙ 12/08/2020

Robustness of Model Predictions under Extension

Often, mathematical models of the real world are simplified representati...

Tineke Blom, et al. ∙

∙ 10/27/2020

A Weaker Faithfulness Assumption based on Triple Interactions

One of the core assumptions in causal discovery is the faithfulness assu...

Alexander Marx, et al. ∙

∙ 09/16/2020

Causal Discovery for Causal Bandits utilizing Separating Sets

The Causal Bandit is a variant of the classic Bandit problem where an ag...

Arnoud A. W. M. de Kroon, et al. ∙

∙ 08/17/2020

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

The performance of constraint-based causal discovery algorithms is promi...

Philip A. Boeken, et al. ∙

∙ 07/14/2020

Conditional Independences and Causal Relations implied by Sets of Equations

Real-world systems are often modelled by sets of equations with exogenou...

Tineke Blom, et al. ∙

∙ 05/01/2020

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...

Joris M. Mooij, et al. ∙

∙ 05/01/2020

Constraint-Based Causal Discovery In The Presence Of Cycles

While feedback loops are known to play important roles in many complex s...

Joris M. Mooij, et al. ∙

∙ 10/06/2019

Boosting Local Causal Discovery in High-Dimensional Expression Data

We study how well Local Causal Discovery (LCD), a simple and efficient c...

Philip Versteeg, et al. ∙

∙ 01/02/2019

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...

Patrick Forré, et al. ∙

∙ 10/18/2018

An Upper Bound for Random Measurement Error in Causal Discovery

Causal discovery algorithms infer causal relations from data based on se...

Tineke Blom, et al. ∙

∙ 07/10/2018

Algebraic Equivalence of Linear Structural Equation Models

Despite their popularity, many questions about the algebraic constraints...

Thijs van Ommen, et al. ∙

∙ 07/09/2018

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 ...

Patrick Forré, et al. ∙

∙ 05/16/2018

Generalized Strucutral Causal Models

Structural causal models are a popular tool to describe causal relations...

Tineke Blom, et al. ∙

∙ 03/23/2018

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

Random Differential Equations provide a natural extension of Ordinary Di...

Stephan Bongers, et al. ∙

∙ 10/24/2017

Markov Properties for Graphical Models with Cycles and Latent Variables

We investigate probabilistic graphical models that allow for both cycles...

Patrick Forré, et al. ∙

∙ 07/20/2017

Causal Transfer Learning

An important goal in both transfer learning and causal inference is to m...

Sara Magliacane, et al. ∙

∙ 07/04/2017

Causal Consistency of Structural Equation Models

Complex systems can be modelled at various levels of detail. Ideally, ca...

Paul K. Rubenstein, et al. ∙

∙ 11/30/2016

Joint Causal Inference from Observational and Experimental Datasets

We introduce Joint Causal Inference (JCI), a powerful formulation of cau...

Sara Magliacane, et al. ∙

∙ 11/18/2016

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

Structural causal models (SCMs), also known as non-parametric structural...

Stephan Bongers, et al. ∙

∙ 06/22/2016

Ancestral Causal Inference

Constraint-based causal discovery from limited data is a notoriously dif...

Sara Magliacane, et al. ∙

∙ 12/11/2014

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is ...

Joris M. Mooij, et al. ∙

∙ 11/06/2014

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

This article contains detailed proofs and additional examples related to...

Tom Claassen, et al. ∙