Search Results for author: Alexander Marx

Found 14 papers, 5 papers with code

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

no code implementations26 Mar 2025 Marlies Hafer, Alexander Marx

Boeken et al. [2023] show that when training data is subject to selection, proxy variables unaffected by this process can, under certain constraints, be used to correct for selection bias to estimate $E[Y|X]$, and hence $E[Y|do(X)]$, reliably.

regression Selection bias

A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany

no code implementations21 Feb 2025 Ina Dormuth, Sven Franke, Marlies Hafer, Tim Katzke, Alexander Marx, Emmanuel Müller, Daniel Neider, Markus Pauly, Jérôme Rutinowski

In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information.

Anomaly Detection by Context Contrasting

no code implementations29 May 2024 Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt

At test time, representations of anomalies that do not adhere to the invariances of normal data then deviate from their respective context cluster.

Self-Supervised Anomaly Detection Self-Supervised Learning +1

On the Properties and Estimation of Pointwise Mutual Information Profiles

1 code implementation16 Oct 2023 Paweł Czyż, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx

The pointwise mutual information profile, or simply profile, is the distribution of pointwise mutual information for a given pair of random variables.

Mutual Information Estimation Uncertainty Quantification

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning

no code implementations11 Oct 2023 Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi

The prior is typically specified as a class of parametric distributions, the design of which can be cumbersome in practice, often resulting in the choice of uninformative priors.

reinforcement-learning Reinforcement Learning

Beyond Normal: On the Evaluation of Mutual Information Estimators

2 code implementations NeurIPS 2023 Paweł Czyż, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx

Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology.

Benchmarking Domain Generalization +1

Identifiability Results for Multimodal Contrastive Learning

1 code implementation16 Mar 2023 Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt

Our work generalizes previous identifiability results by redefining the generative process in terms of distinct mechanisms with modality-specific latent variables.

Contrastive Learning Representation Learning

On the Identifiability and Estimation of Causal Location-Scale Noise Models

1 code implementation13 Oct 2022 Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx

We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i. e., $Y = f(X) + g(X)N$.

Causal Discovery Causal Inference

Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms

1 code implementation13 Jan 2021 Alexander Marx, Lincen Yang, Matthijs van Leeuwen

Further, we show that CMI can be consistently estimated for discrete-continuous mixture variables by learning an adaptive histogram model.

Causal Discovery Information Theory Information Theory Applications

A Weaker Faithfulness Assumption based on Triple Interactions

no code implementations27 Oct 2020 Alexander Marx, Arthur Gretton, Joris M. Mooij

One of the core assumptions in causal discovery is the faithfulness assumption, i. e., assuming that independencies found in the data are due to separations in the true causal graph.

Causal Discovery

Testing Conditional Independence on Discrete Data using Stochastic Complexity

no code implementations12 Mar 2019 Alexander Marx, Jilles Vreeken

Testing for conditional independence is a core aspect of constraint-based causal discovery.

Causal Discovery

Causal Discovery by Telling Apart Parents and Children

no code implementations20 Aug 2018 Alexander Marx, Jilles Vreeken

We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders.

Causal Discovery

Telling Cause from Effect using MDL-based Local and Global Regression

no code implementations26 Sep 2017 Alexander Marx, Jilles Vreeken

We infer $X$ causes $Y$ in case it is shorter to describe $Y$ as a function of $X$ than the inverse direction.

regression

Causal Inference on Multivariate and Mixed-Type Data

no code implementations21 Feb 2017 Alexander Marx, Jilles Vreeken

Given data over the joint distribution of two random variables $X$ and $Y$, we consider the problem of inferring the most likely causal direction between $X$ and $Y$.

Causal Inference Vocal Bursts Type Prediction

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