Dominik Janzing

research

∙ 05/16/2023

Toward Falsifying Causal Graphs Using a Permutation-Based Test

Understanding the causal relationships among the variables of a system i...

0 Elias Eulig, et al. ∙

research

∙ 05/11/2023

Reinterpreting causal discovery as the task of predicting unobserved joint statistics

If X,Y,Z denote sets of random variables, two different data sources may...

0 Dominik Janzing, et al. ∙

research

∙ 05/10/2023

Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts

Statistical prediction models are often trained on data that is drawn fr...

0 Bijan Mazaheri, et al. ∙

research

∙ 04/23/2023

Meaningful Causal Aggregation and Paradoxical Confounding

In aggregated variables the impact of interventions is typically ill-def...

0 Yuchen Zhu, et al. ∙

research

∙ 04/04/2023

Bounding probabilities of causation through the causal marginal problem

Probabilities of Causation play a fundamental role in decision making in...

0 Numair Sani, et al. ∙

research

∙ 11/15/2022

Phenomenological Causality

Discussions on causal relations in real life often consider variables fo...

0 Dominik Janzing, et al. ∙

research

∙ 06/26/2022

Explaining the root causes of unit-level changes

Existing methods of explainable AI and interpretable ML cannot explain c...

0 Kailash Budhathoki, et al. ∙

research

∙ 06/14/2022

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models

We introduce DoWhy-GCM, an extension of the DoWhy Python library, that l...

0 Patrick Blöbaum, et al. ∙

research

∙ 03/08/2022

Score matching enables causal discovery of nonlinear additive noise models

This paper demonstrates how to recover causal graphs from the score of t...

2 Paul Rolland, et al. ∙

research

∙ 02/23/2022

Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies

This paper proposes a new approach for testing Granger non-causality on ...

0 Lenon Minorics, et al. ∙

research

∙ 02/04/2022

Correcting Confounding via Random Selection of Background Variables

We propose a method to distinguish causal influence from hidden confound...

0 You-Lin Chen, et al. ∙

research

∙ 02/02/2022

Causal Inference Through the Structural Causal Marginal Problem

We introduce an approach to counterfactual inference based on merging in...

37 Luigi Gresele, et al. ∙

research

∙ 11/18/2021

Causal Forecasting:Generalization Bounds for Autoregressive Models

Despite the increasing relevance of forecasting methods, the causal impl...

0 Leena Chennuru Vankadara, et al. ∙

research

∙ 10/29/2021

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations

Distinguishing between cause and effect using time series observational ...

17 Michel Besserve, et al. ∙

research

∙ 10/11/2021

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

Predicting the future trajectory of a moving agent can be easy when the ...

10 Osama Makansi, et al. ∙

research

∙ 07/15/2021

Obtaining Causal Information by Merging Datasets with MAXENT

The investigation of the question "which treatment has a causal effect o...

0 Sergio Hernan Garrido Mejia, et al. ∙

research

∙ 02/26/2021

Why did the distribution change?

We describe a formal approach based on graphical causal models to identi...

0 Kailash Budhathoki, et al. ∙

research

∙ 02/07/2021

Causal version of Principle of Insufficient Reason and MaxEnt

The Principle of insufficient Reason (PIR) assigns equal probabilities t...

0 Dominik Janzing, et al. ∙

research

∙ 07/01/2020

Quantifying causal contribution via structure preserving interventions

We introduce 'Causal Information Contribution (CIC)' and 'Causal Varianc...

0 Dominik Janzing, et al. ∙

research

∙ 05/18/2020

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...

4 Atalanti A. Mastakouri, et al. ∙

research

∙ 04/01/2020

A theory of independent mechanisms for extrapolation in generative models

Deep generative models reproduce complex empirical data but cannot extra...

7 Michel Besserve, et al. ∙

research

∙ 12/05/2019

Causal structure based root cause analysis of outliers

We describe a formal approach to identify 'root causes' of outliers obse...

0 Dominik Janzing, et al. ∙

research

∙ 10/29/2019

Feature relevance quantification in explainable AI: A causality problem

We discuss promising recent contributions on quantifying feature relevan...

0 Dominik Janzing, et al. ∙

research

∙ 04/11/2018

Structural causal models for macro-variables in time-series

We consider a bivariate time series (X_t,Y_t) that is given by a simple ...

0 Dominik Janzing, et al. ∙

research

∙ 04/09/2018

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...

0 Dominik Janzing, et al. ∙

research

∙ 03/02/2018

Detecting non-causal artifacts in multivariate linear regression models

We consider linear models where d potential causes X_1,...,X_d are corre...

0 Dominik Janzing, et al. ∙

research

∙ 02/19/2018

Analysis of Cause-Effect Inference via Regression Errors

We address the problem of inferring the causal relation between two vari...

0 Patrick Blöbaum, et al. ∙

research

∙ 07/04/2017

Causal Consistency of Structural Equation Models

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

0 Paul K. Rubenstein, et al. ∙

research

∙ 06/08/2017

Avoiding Discrimination through Causal Reasoning

Recent work on fairness in machine learning has focused on various stati...

0 Niki Kilbertus, et al. ∙

research

∙ 05/05/2017

Group invariance principles for causal generative models

The postulate of independence of cause and mechanism (ICM) has recently ...

0 Michel Besserve, et al. ∙

research

∙ 04/05/2017

Detecting confounding in multivariate linear models via spectral analysis

We study a model where one target variable Y is correlated with a vector...

0 Dominik Janzing, et al. ∙

research

∙ 05/12/2015

Removing systematic errors for exoplanet search via latent causes

We describe a method for removing the effect of confounders in order to ...

0 Bernhard Schölkopf, et al. ∙

research

∙ 03/04/2015

Telling cause from effect in deterministic linear dynamical systems

Inferring a cause from its effect using observed time series data is a m...

0 Naji Shajarisales, et al. ∙

research

∙ 12/11/2014

Distinguishing cause from effect using observational data: methods and benchmarks

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

0 Joris M. Mooij, et al. ∙

research

∙ 11/14/2014

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

A widely applied approach to causal inference from a non-experimental ti...

0 Philipp Geiger, et al. ∙

research

∙ 08/09/2014

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

We show how, and under which conditions, the equilibrium states of a fir...

0 Joris Mooij, et al. ∙

research

∙ 02/11/2014

Justifying Information-Geometric Causal Inference

Information Geometric Causal Inference (IGCI) is a new approach to disti...

0 Dominik Janzing, et al. ∙

research

∙ 12/19/2013

Consistency of Causal Inference under the Additive Noise Model

We analyze a family of methods for statistical causal inference from sam...

0 Samory Kpotufe, et al. ∙

research

∙ 09/26/2013

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

We propose a kernel method to identify finite mixtures of nonparametric ...

0 Eleni Sgouritsa, et al. ∙

research

∙ 09/26/2013

Causal Discovery with Continuous Additive Noise Models

We consider the problem of learning causal directed acyclic graphs from ...

0 Jonas Peters, et al. ∙

research

∙ 07/21/2012

Causal Inference on Time Series using Structural Equation Models

Causal inference uses observations to infer the causal structure of the ...

0 Jonas Peters, et al. ∙

research

∙ 06/27/2012

On Causal and Anticausal Learning

We consider the problem of function estimation in the case where an unde...

0 Bernhard Schoelkopf, et al. ∙

research

∙ 05/09/2012

Identifying confounders using additive noise models

We propose a method for inferring the existence of a latent common cause...

0 Dominik Janzing, et al. ∙

research

∙ 03/15/2012

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery

In nonlinear latent variable models or dynamic models, if we consider th...

0 Kun Zhang, et al. ∙

research

∙ 03/15/2012

Inferring deterministic causal relations

We consider two variables that are related to each other by an invertibl...

0 Povilas Daniusis, et al. ∙

research

∙ 02/14/2012

Testing whether linear equations are causal: A free probability theory approach

We propose a method that infers whether linear relations between two hig...

0 Jakob Zscheischler, et al. ∙

research

∙ 02/14/2012

Kernel-based Conditional Independence Test and Application in Causal Discovery

Conditional independence testing is an important problem, especially in ...

0 Kun Zhang, et al. ∙

research

∙ 02/14/2012

Identifiability of Causal Graphs using Functional Models

This work addresses the following question: Under what assumptions on th...

0 Jonas Peters, et al. ∙

research

∙ 02/14/2012

Detecting low-complexity unobserved causes

We describe a method that infers whether statistical dependences between...

0 Dominik Janzing, et al. ∙

research

∙ 12/12/2011

Robust Learning via Cause-Effect Models

We consider the problem of function estimation in the case where the dat...

0 Bernhard Schölkopf, et al. ∙

Dominik Janzing

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