Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm

no code implementations • 28 May 2024 • Mathieu Chevalley, Patrick Schwab, Arash Mehrjou

More specifically, we introduce the notion of interventional faithfulness, which relies on comparisons between the marginal distributions of each variable across observational and interventional settings, and we introduce a score on causal orders.

The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data

no code implementations • 29 Aug 2023 • Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani, Pascal Notin, Artemy Bakulin, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab

In drug discovery, mapping interactions between genes within cellular systems is a crucial early step.

Drug Discovery

CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

2 code implementations • 31 Oct 2022 • Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou, Jure Leskovec, Patrick Schwab

Traditional evaluations conducted on synthetic datasets do not reflect the performance in real-world systems.

Causal Discovery Causal Inference +1

Invariant Causal Mechanisms through Distribution Matching

1 code implementation • 23 Jun 2022 • Mathieu Chevalley, Charlotte Bunne, Andreas Krause, Stefan Bauer

Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks.

Domain Generalization