12 citations · 26 across the 9 of their papers we have counts for
9 papers
Theoretical Guarantees for Causal Discovery on Large Random Graphs
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
We investigate theoretical guarantees for the false-negative rate (FNR) -- the fraction of true causal edges whose orientation is not recovered, under single-variable random interv…
In-silico biological discovery with large perturbation models
Djordje Miladinovic, Tobias Höppe, Mathieu Chevalley +6
Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from u…
Multi-megabase scale genome interpretation with genetic language models
Frederik Träuble, Lachlan Stuart, Andreas Georgiou +9
Understanding how molecular changes caused by genetic variation drive disease risk is crucial for deciphering disease mechanisms. However, interpreting genome sequences is challeng…
Efficient Differentiable Discovery of Causal Order
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
In the algorithm Intersort, Chevalley et al. (2024) proposed a score-based method to discover the causal order of variables in a Directed Acyclic Graph (DAG) model, leveraging inte…
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Targeted and uniform interventions to a system are crucial for unveiling causal relationships. While several methods have been developed to leverage interventional data for causal…
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani +15
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. Such maps are not only foundational for understanding the molecular mechanism…
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou +2
Causal inference is a vital aspect of multiple scientific disciplines and is routinely applied to high-impact applications such as medicine. However, evaluating the performance of…
Invariant Causal Mechanisms through Distribution Matching
Mathieu Chevalley, Charlotte Bunne, Andreas Krause +1
Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks. One key property for robustness…
By the user, for the user: A user-centric approach to quantifying the privacy of websites
Matius Chairani, Mathieu Chevalley, Abderrahmane Lazraq +1
Third-party tracking is common on almost all commercially operated websites. Prior work has studied in detail the extent of third-party tracking on the web, detection of third-part…