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07/19/2023
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
To avoid failures on out-of-distribution data, recent works have sought ...
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06/09/2023
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators
We study causal effect estimation from a mixture of observational and in...
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06/01/2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions
We study causal representation learning, the task of inferring latent ca...
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05/26/2023
Causal Component Analysis
Independent Component Analysis (ICA) aims to recover independent latent ...
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05/23/2023
Provably Learning Object-Centric Representations
Learning structured representations of the visual world in terms of obje...
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12/14/2022
Evaluating vaccine allocation strategies using simulation-assisted causal modelling
Early on during a pandemic, vaccine availability is limited, requiring p...
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11/01/2022
Backtracking Counterfactuals
Counterfactual reasoning – envisioning hypothetical scenarios, or possib...
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10/01/2022
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
In representation learning, a common approach is to seek representations...
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06/06/2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Variational autoencoders (VAEs) are a popular framework for modeling com...
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06/04/2022
Active Bayesian Causal Inference
Causal discovery and causal reasoning are classically treated as separat...
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06/04/2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Machine learning approaches commonly rely on the assumption of independe...
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04/01/2022
From Statistical to Causal Learning
We describe basic ideas underlying research to build and understand arti...
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02/14/2022
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"
Model identifiability is a desirable property in the context of unsuperv...
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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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10/13/2021
Unsupervised Object Learning via Common Fate
Learning generative object models from unlabelled videos is a long stand...
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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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10/07/2021
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning in NLP
The principle of independent causal mechanisms (ICM) states that generat...
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07/17/2021
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
An important component for generalization in machine learning is to unco...
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07/02/2021
Backward-Compatible Prediction Updates: A Probabilistic Approach
When machine learning systems meet real world applications, accuracy is ...
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06/22/2021
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
Algorithmic recourse aims to provide actionable recommendations to indiv...
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06/09/2021
Independent mechanism analysis, a new concept?
Independent component analysis provides a principled framework for unsup...
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06/08/2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Self-supervised representation learning has shown remarkable success in ...
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10/13/2020
On the Fairness of Causal Algorithmic Recourse
While many recent works have studied the problem of algorithmic fairness...
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10/01/2020
Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes
Two-sample and independence tests with the kernel-based MMD and HSIC hav...
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06/11/2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Recent work has discussed the limitations of counterfactual explanations...
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05/14/2020
Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
We point out an example of Simpson's paradox in COVID-19 case fatality r...
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04/27/2020
Towards causal generative scene models via competition of experts
Learning how to model complex scenes in a modular way with recombinable ...
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04/16/2020
Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals
The United Nations' ambitions to combat climate change and prosper human...
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10/09/2019
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
We study the problem of causal discovery through targeted interventions....
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05/28/2019
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully u...
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07/20/2018