
Why did the distribution change?
We describe a formal approach based on graphical causal models to identi...
read it

Causal version of Principle of Insufficient Reason and MaxEnt
The Principle of insufficient Reason (PIR) assigns equal probabilities t...
read it

Quantifying causal contribution via structure preserving interventions
We introduce 'Causal Information Contribution (CIC)' and 'Causal Varianc...
read it

Necessary and sufficient conditions for causal feature selection in time series with latent common causes
We study the identification of direct and indirect causes on time series...
read it

A theory of independent mechanisms for extrapolation in generative models
Deep generative models reproduce complex empirical data but cannot extra...
read it

Causal structure based root cause analysis of outliers
We describe a formal approach to identify 'root causes' of outliers obse...
read it

Feature relevance quantification in explainable AI: A causality problem
We discuss promising recent contributions on quantifying feature relevan...
read it

Structural causal models for macrovariables in timeseries
We consider a bivariate time series (X_t,Y_t) that is given by a simple ...
read it

Merging joint distributions via causal model classes with low VC dimension
If X,Y,Z denote sets of random variables, two different data sources may...
read it

Detecting noncausal artifacts in multivariate linear regression models
We consider linear models where d potential causes X_1,...,X_d are corre...
read it

Analysis of CauseEffect Inference via Regression Errors
We address the problem of inferring the causal relation between two vari...
read it

Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, ca...
read it

Avoiding Discrimination through Causal Reasoning
Recent work on fairness in machine learning has focused on various stati...
read it

Group invariance principles for causal generative models
The postulate of independence of cause and mechanism (ICM) has recently ...
read it

Detecting confounding in multivariate linear models via spectral analysis
We study a model where one target variable Y is correlated with a vector...
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

Telling cause from effect in deterministic linear dynamical systems
Inferring a cause from its effect using observed time series data is a m...
read it

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

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
A widely applied approach to causal inference from a nonexperimental ti...
read it

From Ordinary Differential Equations to Structural Causal Models: the deterministic case
We show how, and under which conditions, the equilibrium states of a fir...
read it

Justifying InformationGeometric Causal Inference
Information Geometric Causal Inference (IGCI) is a new approach to disti...
read it

Consistency of Causal Inference under the Additive Noise Model
We analyze a family of methods for statistical causal inference from sam...
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

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

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

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
In nonlinear latent variable models or dynamic models, if we consider th...
read it

Inferring deterministic causal relations
We consider two variables that are related to each other by an invertibl...
read it

Testing whether linear equations are causal: A free probability theory approach
We propose a method that infers whether linear relations between two hig...
read it

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

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

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

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

Is there a physically universal cellular automaton or Hamiltonian?
It is known that both quantum and classical cellular automata (CA) exist...
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

Distinguishing Cause and Effect via Second Order Exponential Models
We propose a method to infer causal structures containing both discrete ...
read it

Telling cause from effect based on highdimensional observations
We describe a method for inferring linear causal relations among multid...
read it

Causal inference using the algorithmic Markov condition
Inferring the causal structure that links n observables is usually based...
read it
Dominik Janzing
is this you? claim profile
Senior Scientist at Max Planck Institute for Intelligent Systems