In additive noise models (ANMs), the ordering of variables by marginal variances may be indicative of the causal order. We introduce varsortability as a measure of this agreement between orderings. Since varsortability is high in simulated ANMs, we advocate reporting varsortability when benchmarking.

pdf / arXiv / bibWe argue that compositionality is a desideratum for causal model transformations and the associated errors. We introduce a category of finite interventional causal models and, leveraging theory of enriched categories, prove that our framework enjoys the desired compositionality properties.

pdf / arXiv / bibJournal of Cognitive Neuroscience, 33(2):226–247, 2021

We outline why we believe that distributional robustness and model generalisability can be useful for guiding causality research in cognitive neuroscience. In particular, it can help with respect to the scarcity of targeted interventional data and the difficulty of defining the right variables. We provide an accessible introduction to causality and review selected causal discovery approaches and their underlying ideas, assumptions, and problems.

pdf / doi / arXiv / bibProceedings of the NeurIPS 2019 Competition and Demonstration Track, Proceedings of Machine Learning Research, 123:27−36, 2020

We describe the algorithms for causal structure learning from time series data that **won the NeurIPS competition** »Causality 4 Climate« 2019. We examine why large regression coefficients may predict causal links better in practice than small p-values and thus why normalising the data may sometimes hinder causal structure learning. The algorithms are available at tidybench.

Journal of Machine Learning Research, 20(147):1−50, 2019

Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations between SEMs.

pdf / JMLR / arXiv / Python/R/matlab / audible example / EEG example video / bibUncertainty in Artificial Intelligence (UAI), 2017

Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations between SEMs.

pdf / arXiv / bibPLoS ONE, 12(6):e0180136, 2017

We find that electroencephalography (EEG) correlates of self-referential thinking are present in healthy individuals, but not in those with ALS. In particular, thinking about themselves or others significantly modulates the bandpower in the medial prefrontal cortex in healthy individuals, but not in ALS patients.

pdf / doi / bibIEEE International Conference on Systems, Man, and Cybernetics (SMC 2017), 2017

We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES).

pdf / doi / arXiv / bibIEEE Journal of Selected Topics in Signal Processing, 10(7):1254–1266, 2016

MERLiN is a causal inference algorithm that can recover from an observed linear mixture a causal variable that is an effect of another given variable. MERLiN implements a novel idea on how to (re-)construct causal variables and is robust against hidden confounding.

pdf / doi / arXiv / code / bibJournal of Machine Learning Research, 17(137):1–5, 2016

Pymanopt lowers the barriers to users wishing to use state of the art manifold optimization techniques, by using automated differentiation for calculating derivative information, saving users time and saving them from potential calculation and implementation errors.

(Example: manifold optimisation for inferring parameters of a MoG model.)

International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016

This paper proposes an extension of the MERLiN algorithm to identify non-linear cause-effect relationships between linearly mixed neuroimaging data.

pdf / doi / arXiv / code / slides / bibNeuroImage, 110:48–59, 2015

We provide a set of rules which causal statements are warranted and which ones are not supported by empirical evidence. Only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models, however, we obtain insights into causal relations beyond those that are implied by each individual model type.

pdf / doi / arXiv / explainer / slides / bibInternational Workshop on Pattern Recognition in Neuroimaging (PRNI), 2014

In this paper, we argue that it is not sufficient to distinguish between encoding- and decoding models: The interpretation of such models depends on whether they are employed in a stimulus- or response-based setting.

pdf / doi / arXiv / bibInternational Workshop on Cognitive Information Processing (CIP), 2014

We show that index finger positions can be differentiated from non-invasive EEG recordings in healthy human subjects. High β-power (20–30 Hz) over contralateral sensorimotor cortex carried most information about finger position.

This work won the **best student paper award**.

ETH Zurich, 2019

The statistical treatment of causal modelling lays out methodology that, under well specified assumptions, enables us to infer cause-effect relationships from observational data. The adoption and fruitful utilisation of such methods remains limited, however, despite the statistical foundations and numerous theoretical advances. In this thesis, we present contributions towards closing the gap between statistical causal modelling and its successful application.

pdf / doi / bibIn a collaboration with the University Heart Center Zurich and the ETH Zurich we are developing robust models for predicting 1-year mortality after acute coronary syndromes. Our approach is robust, interpretable, and only uses objective measurements as input instead of subjective assessments by clinicians.

2013

This seminar paper reviews Kurt Gödel’s article »What is Cantor’s continuum problem?«. As this paper aims to be almost self-contained, short recaps, rough explanations and selective examples are provided where appropriate.

pdf**coroICA.**- Confounding-robust Independent Component Analysis.

Python / R / matlab / documentation **Pymanopt.**- Python toolbox for optimisation on manifolds with support for automatic differentiation.

Part of the manopt family.

code / documentation **MERLiN non- & linear.**- Mixture Effect Recovery in Linear Networks.

code **tidybench.**- TIme series DiscoverY BENCHmark implementation of our competition-winning algorithms.

code

- Artificial Intelligence and Statistics (AISTATS)
- International Conference on Machine Learning (ICML; top 5% reviewer 2019)
- Journal of Cognitive Neuroscience (JoCN)
- Journal of Machine Learning Research (JMLR)
- Journal of the Royal Statistical Society (Series C)
- Neural Information Processing Systems (NeurIPS)
- NeuroImage
- Uncertainty in Artificial Intelligence (UAI)