
Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions
Unobserved confounding is one of the main challenges when estimating cau...
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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...
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Robustness of Model Predictions under Extension
Often, mathematical models of the real world are simplified representati...
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A Weaker Faithfulness Assumption based on Triple Interactions
One of the core assumptions in causal discovery is the faithfulness assu...
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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...
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A Bayesian Nonparametric Conditional Twosample Test with an Application to Local Causal Discovery
The performance of constraintbased causal discovery algorithms is promi...
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Conditional Independences and Causal Relations implied by Sets of Equations
Realworld systems are often modelled by sets of equations with exogenou...
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ConstraintBased Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex s...
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ConstraintBased Causal Discovery In The Presence Of Cycles
While feedback loops are known to play important roles in many complex s...
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Boosting Local Causal Discovery in HighDimensional Expression Data
We study how well Local Causal Discovery (LCD), a simple and efficient c...
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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 docalculus) for...
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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...
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Algebraic Equivalence of Linear Structural Equation Models
Despite their popularity, many questions about the algebraic constraints...
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Constraintbased Causal Discovery for NonLinear Structural Causal Models with Cycles and Latent Confounders
We address the problem of causal discovery from data, making use of the ...
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Generalized Strucutral Causal Models
Structural causal models are a popular tool to describe causal relations...
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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...
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Markov Properties for Graphical Models with Cycles and Latent Variables
We investigate probabilistic graphical models that allow for both cycles...
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Causal Transfer Learning
An important goal in both transfer learning and causal inference is to m...
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Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, ca...
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Joint Causal Inference from Observational and Experimental Datasets
We introduce Joint Causal Inference (JCI), a powerful formulation of cau...
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Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions
Structural causal models (SCMs), also known as nonparametric structural...
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Ancestral Causal Inference
Constraintbased causal discovery from limited data is a notoriously dif...
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Distinguishing cause from effect using observational data: methods and benchmarks
The discovery of causal relationships from purely observational data is ...
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Proof Supplement  Learning Sparse Causal Models is not NPhard (UAI2013)
This article contains detailed proofs and additional examples related to...
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Joris M. Mooij
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