12 citations · 26 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…