
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions
Structural causal models (SCMs), also known as nonparametric structural...
read it

Kernelbased Tests for Joint Independence
We investigate the problem of testing whether d random variables, which ...
read it

backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the p...
read it

Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to ...
read it

Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
read it

Distinguishing cause from effect using observational data: methods and benchmarks
The discovery of causal relationships from purely observational data is ...
read it

Counterfactual Reasoning and Learning Systems
This work shows how to leverage causal inference to understand the behav...
read it

On the Intersection Property of Conditional Independence and its Application to Causal Discovery
This work investigates the intersection property of conditional independ...
read it

CAM: Causal additive models, highdimensional order search and penalized regression
We develop estimation for potentially highdimensional additive structur...
read it

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
We propose a kernel method to identify finite mixtures of nonparametric ...
read it

Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from ...
read it

Structural Intervention Distance (SID) for Evaluating Causal Graphs
Causal inference relies on the structure of a graph, often a directed ac...
read it

Causal Inference on Time Series using Structural Equation Models
Causal inference uses observations to infer the causal structure of the ...
read it

On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an unde...
read it

Identifiability of Gaussian structural equation models with equal error variances
We consider structural equation models in which variables can be written...
read it

Identifying confounders using additive noise models
We propose a method for inferring the existence of a latent common cause...
read it

Detecting lowcomplexity unobserved causes
We describe a method that infers whether statistical dependences between...
read it

Identifiability of Causal Graphs using Functional Models
This work addresses the following question: Under what assumptions on th...
read it

Kernelbased Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in ...
read it

Robust Learning via CauseEffect Models
We consider the problem of function estimation in the case where the dat...
read it

Causal Inference on Discrete Data using Additive Noise Models
Inferring the causal structure of a set of random variables from a finit...
read it

Anchor regression: heterogeneous data meets causality
This is a preliminary draft of "Anchor regression: heterogeneous data me...
read it

The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
It is a common saying that testing for conditional independence, i.e., t...
read it

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 ...
read it

Identifying Causal Structure in LargeScale Kinetic Systems
In the natural sciences, differential equations are widely used to descr...
read it

Causal discovery in heavytailed models
Causal questions are omnipresent in many scientific problems. While much...
read it

Stabilizing Variable Selection and Regression
We consider regression in which one predicts a response Y with a set of ...
read it

Causal models for dynamical systems
A probabilistic model describes a system in its observational state. In ...
read it

Distributional robustness as a guiding principle for causality in cognitive neuroscience
While probabilistic models describe the dependence structure between obs...
read it
Jonas Peters
is this you? claim profile
Associate Professor in Statistics at University of Copenhagen