Search Results for author: Julius von Kügelgen

Found 28 papers, 17 papers with code

Nonparametric Identifiability of Causal Representations from Unknown Interventions

no code implementations1 Jun 2023 Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekić, Elias Bareinboim, David M. Blei, Bernhard Schölkopf

We study the fundamental setting of two causal variables and prove that the observational distribution and one perfect intervention per node suffice for identifiability, subject to a genericity condition.

Causal Component Analysis

no code implementations26 May 2023 Wendong Liang, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf

As a corollary, this interventional perspective also leads to new identifiability results for nonlinear ICA -- a special case of CauCA with an empty graph -- requiring strictly fewer datasets than previous results.

Representation Learning

Provably Learning Object-Centric Representations

no code implementations23 May 2023 Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel

Under this generative process, we prove that the ground-truth object representations can be identified by an invertible and compositional inference model, even in the presence of dependencies between objects.

Representation Learning

Evaluating vaccine allocation strategies using simulation-assisted causal modelling

1 code implementation14 Dec 2022 Armin Kekić, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf

Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.

Backtracking Counterfactuals

no code implementations1 Nov 2022 Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers

In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared.

Explainable artificial intelligence

Probable Domain Generalization via Quantile Risk Minimization

2 code implementations20 Jul 2022 Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$.

Domain Generalization

Active Bayesian Causal Inference

1 code implementation4 Jun 2022 Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen

In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest.

Active Learning Causal Discovery +2

From Statistical to Causal Learning

no code implementations1 Apr 2022 Bernhard Schölkopf, Julius von Kügelgen

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality.

BIG-bench Machine Learning

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.

Trajectory Prediction

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP

1 code implementation EMNLP 2021 Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf

The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.

Causal Inference Domain Adaptation

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

1 code implementation ICLR 2022 Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.

Representation Learning

Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects

no code implementations22 Jun 2021 Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf

Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system.

Decision Making

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

1 code implementation NeurIPS 2020 Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.

Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects

1 code implementation14 May 2020 Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf

We point out limitations and extensions for future work, and, finally, discuss the role of causal reasoning in the broader context of using AI to combat the Covid-19 pandemic.

Applications Methodology

Towards causal generative scene models via competition of experts

no code implementations27 Apr 2020 Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf

Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world.

Inductive Bias

Semi-Supervised Learning, Causality and the Conditional Cluster Assumption

1 code implementation28 May 2019 Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf

While the success of semi-supervised learning (SSL) is still not fully understood, Sch\"olkopf et al. (2012) have established a link to the principle of independent causal mechanisms.

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

1 code implementation20 Jul 2018 Julius von Kügelgen, Alexander Mey, Marco Loog

Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only.

Domain Adaptation

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