Search Results for author: Dominik Janzing

Found 55 papers, 8 papers with code

Self-Compatibility: Evaluating Causal Discovery without Ground Truth

1 code implementation18 Jul 2023 Philipp M. Faller, Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing

In this work, we propose a novel method for falsifying the output of a causal discovery algorithm in the absence of ground truth.

Causal Discovery Model Selection

Toward Falsifying Causal Graphs Using a Permutation-Based Test

no code implementations16 May 2023 Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum, Michaela Hardt, Dominik Janzing

By comparing the number of inconsistencies with those on the surrogate baseline, we derive an interpretable metric that captures whether the DAG fits significantly better than random.

Reinterpreting causal discovery as the task of predicting unobserved joint statistics

no code implementations11 May 2023 Dominik Janzing, Philipp M. Faller, Leena Chennuru Vankadara

Here, causal discovery becomes more modest and better accessible to empirical tests than usual: rather than trying to find a causal hypothesis that is `true' a causal hypothesis is {\it useful} whenever it correctly predicts statistical properties of unobserved joint distributions.

Causal Discovery Causal Inference +1

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

no code implementations10 May 2023 Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt

Statistical prediction models are often trained on data from different probability distributions than their eventual use cases.

counterfactual feature selection

Meaningful Causal Aggregation and Paradoxical Confounding

no code implementations23 Apr 2023 Yuchen Zhu, Kailash Budhathoki, Jonas Kuebler, Dominik Janzing

On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions.

Bounding probabilities of causation through the causal marginal problem

no code implementations4 Apr 2023 Numair Sani, Atalanti A. Mastakouri, Dominik Janzing

In the absence of such assumptions, existing work requires multiple observations of datasets that contain the same treatment and outcome variables, in order to establish bounds on these probabilities.

counterfactual Decision Making

Phenomenological Causality

no code implementations15 Nov 2022 Dominik Janzing, Sergio Hernan Garrido Mejia

Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure.

Explaining the root causes of unit-level changes

no code implementations26 Jun 2022 Kailash Budhathoki, George Michailidis, Dominik Janzing

Existing methods of explainable AI and interpretable ML cannot explain change in the values of an output variable for a statistical unit in terms of the change in the input values and the change in the "mechanism" (the function transforming input to output).

Score matching enables causal discovery of nonlinear additive noise models

no code implementations8 Mar 2022 Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello

This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models.

Causal Discovery

Correcting Confounding via Random Selection of Background Variables

no code implementations4 Feb 2022 You-Lin Chen, Lenon Minorics, Dominik Janzing

We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of background features, we propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y with respect to selecting different background features.

Causal Forecasting:Generalization Bounds for Autoregressive Models

1 code implementation18 Nov 2021 Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts, albeit with additional structure (restriction to interventional distributions under the VAR model).

Learning Theory Time Series +1

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

no code implementations29 Oct 2021 Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf

A new perspective has been provided based on the principle of Independence of Causal Mechanisms (ICM), leading to the Spectral Independence Criterion (SIC), postulating that the power spectral density (PSD) of the cause time series is uncorrelated with the squared modulus of the frequency response of the filter generating the effect.

Causal Discovery Causal Inference +2

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

no code implementations ICLR 2022 Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions.

Attribute Trajectory Prediction

Obtaining Causal Information by Merging Datasets with MAXENT

no code implementations15 Jul 2021 Sergio Hernan Garrido Mejia, Elke Kirschbaum, Dominik Janzing

Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders.

Why did the distribution change?

no code implementations26 Feb 2021 Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng

We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables.

Attribute

Causal versions of Maximum Entropy and Principle of Insufficient Reason

no code implementations7 Feb 2021 Dominik Janzing

The Principle of Insufficient Reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other.

Causal Inference

Quantifying intrinsic causal contributions via structure preserving interventions

no code implementations1 Jul 2020 Dominik Janzing, Patrick Blöbaum, Atalanti A. Mastakouri, Philipp M. Faller, Lenon Minorics, Kailash Budhathoki

We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG.

Necessary and sufficient conditions for causal feature selection in time series with latent common causes

no code implementations18 May 2020 Atalanti A. Mastakouri, Bernhard Schölkopf, Dominik Janzing

We study the identification of direct and indirect causes on time series and provide conditions in the presence of latent variables, which we prove to be necessary and sufficient under some graph constraints.

feature selection Time Series +1

A theory of independent mechanisms for extrapolation in generative models

no code implementations1 Apr 2020 Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf

Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments?

Causal structure based root cause analysis of outliers

no code implementations5 Dec 2019 Dominik Janzing, Kailash Budhathoki, Lenon Minorics, Patrick Blöbaum

We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots, X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG).

valid

Perceiving the arrow of time in autoregressive motion

no code implementations NeurIPS 2019 Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann

We employ a so-called frozen noise paradigm enabling us to compare human performance with four different algorithms on a trial-by-trial basis: A causal inference algorithm exploiting the dependence structure of additive noise terms, a neurally inspired network, a Bayesian ideal observer model as well as a simple heuristic.

Causal Inference Time Series Analysis

Selecting causal brain features with a single conditional independence test per feature

no code implementations NeurIPS 2019 Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing

We propose a constraint-based causal feature selection method for identifying causes of a given target variable, selecting from a set of candidate variables, while there can also be hidden variables acting as common causes with the target.

feature selection

Feature relevance quantification in explainable AI: A causal problem

no code implementations29 Oct 2019 Dominik Janzing, Lenon Minorics, Patrick Blöbaum

We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features.

Causal Regularization

1 code implementation NeurIPS 2019 Dominik Janzing

I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample regime.

Learning Theory regression

Merging joint distributions via causal model classes with low VC dimension

no code implementations9 Apr 2018 Dominik Janzing

Here, causal inference becomes more modest and better accessible to empirical tests than usual: rather than trying to find a causal hypothesis that is 'true' (which is a problematic term when it is unclear how to define interventions) a causal hypothesis is useful whenever it correctly predicts statistical properties of unobserved joint distributions.

Statistics Theory Statistics Theory

Detecting non-causal artifacts in multivariate linear regression models

no code implementations ICML 2018 Dominik Janzing, Bernhard Schoelkopf

We consider linear models where $d$ potential causes $X_1,..., X_d$ are correlated with one target quantity $Y$ and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes.

regression

Analysis of cause-effect inference by comparing regression errors

no code implementations19 Feb 2018 Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions.

Causal Inference regression

Avoiding Discrimination through Causal Reasoning

no code implementations NeurIPS 2017 Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf

Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.

Attribute Fairness

Group invariance principles for causal generative models

no code implementations5 May 2017 Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing

The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms.

BIG-bench Machine Learning Causal Discovery

Detecting confounding in multivariate linear models via spectral analysis

no code implementations5 Apr 2017 Dominik Janzing, Bernhard Schoelkopf

We study a model where one target variable Y is correlated with a vector X:=(X_1,..., X_d) of predictor variables being potential causes of Y.

Telling cause from effect in deterministic linear dynamical systems

no code implementations4 Mar 2015 Naji Shajarisales, Dominik Janzing, Bernhard Shoelkopf, Michel Besserve

Assuming the effect is generated by the cause trough a linear system, we propose a new approach based on the hypothesis that nature chooses the "cause" and the "mechanism that generates the effect from the cause" independent of each other.

Causal Discovery Causal Inference +2

Distinguishing cause from effect using observational data: methods and benchmarks

no code implementations11 Dec 2014 Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf

We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data.

Causal Discovery Causal Inference +1

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

no code implementations14 Nov 2014 Philipp Geiger, Kun Zhang, Mingming Gong, Dominik Janzing, Bernhard Schölkopf

A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally.

Causal Inference Time Series +2

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

no code implementations9 Aug 2014 Joris Mooij, Dominik Janzing, Bernhard Schoelkopf

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).

Inferring causal structure: a quantum advantage

no code implementations19 Jun 2014 Katja Ried, Megan Agnew, Lydia Vermeyden, Dominik Janzing, Robert W. Spekkens, Kevin J. Resch

The problem of using observed correlations to infer causal relations is relevant to a wide variety of scientific disciplines.

Causal Inference Relation

Justifying Information-Geometric Causal Inference

no code implementations11 Feb 2014 Dominik Janzing, Bastian Steudel, Naji Shajarisales, Bernhard Schölkopf

Information Geometric Causal Inference (IGCI) is a new approach to distinguish between cause and effect for two variables.

Causal Inference

Consistency of Causal Inference under the Additive Noise Model

no code implementations19 Dec 2013 Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf

We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model.

Causal Inference

Causal Inference on Time Series using Restricted Structural Equation Models

no code implementations NeurIPS 2013 Jonas Peters, Dominik Janzing, Bernhard Schölkopf

We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo).

Causal Inference Time Series +1

Causal Discovery with Continuous Additive Noise Models

no code implementations26 Sep 2013 Jonas Peters, Joris Mooij, Dominik Janzing, Bernhard Schölkopf

We consider the problem of learning causal directed acyclic graphs from an observational joint distribution.

Causal Discovery regression

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

no code implementations30 Apr 2013 Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).

On Causal and Anticausal Learning

1 code implementation27 Jun 2012 Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij

We consider the problem of function estimation in the case where an underlying causal model can be inferred.

Transfer Learning

Quantifying causal influences

no code implementations29 Mar 2012 Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf

Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy.

Statistics Theory Statistics Theory

Kernel-based Conditional Independence Test and Application in Causal Discovery

2 code implementations14 Feb 2012 Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf

Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.

Causal Discovery

On Causal Discovery with Cyclic Additive Noise Models

no code implementations NeurIPS 2011 Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise.

Causal Discovery regression

Probabilistic latent variable models for distinguishing between cause and effect

no code implementations NeurIPS 2010 Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive).

Model Selection

Causal inference using the algorithmic Markov condition

no code implementations23 Apr 2008 Dominik Janzing, Bernhard Schoelkopf

We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs.

Causal Inference

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