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07/18/2023
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
As causal ground truth is incredibly rare, causal discovery algorithms a...
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05/16/2023
Toward Falsifying Causal Graphs Using a Permutation-Based Test
Understanding the causal relationships among the variables of a system i...
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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...
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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...
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04/23/2023
Meaningful Causal Aggregation and Paradoxical Confounding
In aggregated variables the impact of interventions is typically ill-def...
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04/04/2023
Bounding probabilities of causation through the causal marginal problem
Probabilities of Causation play a fundamental role in decision making in...
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11/15/2022
Phenomenological Causality
Discussions on causal relations in real life often consider variables fo...
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06/26/2022
Explaining the root causes of unit-level changes
Existing methods of explainable AI and interpretable ML cannot explain c...
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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...
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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...
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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 ...
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02/04/2022
Correcting Confounding via Random Selection of Background Variables
We propose a method to distinguish causal influence from hidden confound...
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02/02/2022
Causal Inference Through the Structural Causal Marginal Problem
We introduce an approach to counterfactual inference based on merging in...
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11/18/2021
Causal Forecasting:Generalization Bounds for Autoregressive Models
Despite the increasing relevance of forecasting methods, the causal impl...
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10/29/2021
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations
Distinguishing between cause and effect using time series observational ...
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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 ...
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07/15/2021
Obtaining Causal Information by Merging Datasets with MAXENT
The investigation of the question "which treatment has a causal effect o...
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02/26/2021
Why did the distribution change?
We describe a formal approach based on graphical causal models to identi...
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02/07/2021
Causal version of Principle of Insufficient Reason and MaxEnt
The Principle of insufficient Reason (PIR) assigns equal probabilities t...
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07/01/2020
Quantifying causal contribution via structure preserving interventions
We introduce 'Causal Information Contribution (CIC)' and 'Causal Varianc...
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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...
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04/01/2020
A theory of independent mechanisms for extrapolation in generative models
Deep generative models reproduce complex empirical data but cannot extra...
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12/05/2019
Causal structure based root cause analysis of outliers
We describe a formal approach to identify 'root causes' of outliers obse...
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10/29/2019
Feature relevance quantification in explainable AI: A causality problem
We discuss promising recent contributions on quantifying feature relevan...
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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 ...
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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...
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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...
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02/19/2018
Analysis of Cause-Effect Inference via Regression Errors
We address the problem of inferring the causal relation between two vari...
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07/04/2017
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, ca...
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06/08/2017
Avoiding Discrimination through Causal Reasoning
Recent work on fairness in machine learning has focused on various stati...
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05/05/2017
Group invariance principles for causal generative models
The postulate of independence of cause and mechanism (ICM) has recently ...
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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...
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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 ...
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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...
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12/11/2014
Distinguishing cause from effect using observational data: methods and benchmarks
The discovery of causal relationships from purely observational data is ...
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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...
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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...
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02/11/2014
Justifying Information-Geometric Causal Inference
Information Geometric Causal Inference (IGCI) is a new approach to disti...
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12/19/2013
Consistency of Causal Inference under the Additive Noise Model
We analyze a family of methods for statistical causal inference from sam...
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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 ...
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09/26/2013
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from ...
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07/21/2012
Causal Inference on Time Series using Structural Equation Models
Causal inference uses observations to infer the causal structure of the ...
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06/27/2012
On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an unde...
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05/09/2012
Identifying confounders using additive noise models
We propose a method for inferring the existence of a latent common cause...
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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...
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03/15/2012
Inferring deterministic causal relations
We consider two variables that are related to each other by an invertibl...
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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...
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02/14/2012
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in ...
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02/14/2012
Identifiability of Causal Graphs using Functional Models
This work addresses the following question: Under what assumptions on th...
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02/14/2012
