
Regularizing towards Causal Invariance: Linear Models with Proxies
We propose a method for learning linear models whose predictive performa...
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Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
We study the problem of testing the null hypothesis that X and Y are con...
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The Difficult Task of Distribution Generalization in Nonlinear Models
We consider the problem of predicting a response from a set of covariate...
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Towards Causal Inference for SpatioTemporal Data: Conflict and Forest Loss in Colombia
In many data scientific problems, we are interested not only in modeling...
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Distributional Robustness of Kclass Estimators and the PULSE
In causal settings, such as instrumental variable settings, it is well k...
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Distributional robustness as a guiding principle for causality in cognitive neuroscience
While probabilistic models describe the dependence structure between obs...
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Causal models for dynamical systems
A probabilistic model describes a system in its observational state. In ...
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Stabilizing Variable Selection and Regression
We consider regression in which one predicts a response Y with a set of ...
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Causal discovery in heavytailed models
Causal questions are omnipresent in many scientific problems. While much...
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Identifying Causal Structure in LargeScale Kinetic Systems
In the natural sciences, differential equations are widely used to descr...
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Switching Regression Models and Causal Inference in the Presence of Latent Variables
Given a response Y and a vector X = (X^1, ..., X^d) of d predictors, we ...
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The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
It is a common saying that testing for conditional independence, i.e., t...
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Anchor regression: heterogeneous data meets causality
This is a preliminary draft of "Anchor regression: heterogeneous data me...
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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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Kernelbased Tests for Joint Independence
We investigate the problem of testing whether d random variables, which ...
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backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the p...
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Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to ...
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Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
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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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On the Intersection Property of Conditional Independence and its Application to Causal Discovery
This work investigates the intersection property of conditional independ...
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CAM: Causal additive models, highdimensional order search and penalized regression
We develop estimation for potentially highdimensional additive structur...
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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric ...
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Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from ...
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Structural Intervention Distance (SID) for Evaluating Causal Graphs
Causal inference relies on the structure of a graph, often a directed ac...
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Counterfactual Reasoning and Learning Systems
This work shows how to leverage causal inference to understand the behav...
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Causal Inference on Time Series using Structural Equation Models
Causal inference uses observations to infer the causal structure of the ...
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On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an unde...
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Identifiability of Gaussian structural equation models with equal error variances
We consider structural equation models in which variables can be written...
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Identifying confounders using additive noise models
We propose a method for inferring the existence of a latent common cause...
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Kernelbased Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in ...
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Identifiability of Causal Graphs using Functional Models
This work addresses the following question: Under what assumptions on th...
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Detecting lowcomplexity unobserved causes
We describe a method that infers whether statistical dependences between...
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Robust Learning via CauseEffect Models
We consider the problem of function estimation in the case where the dat...
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Causal Inference on Discrete Data using Additive Noise Models
Inferring the causal structure of a set of random variables from a finit...
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Jonas Peters
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Associate Professor in Statistics at University of Copenhagen