75 citations · 381 across the 17 of their papers we have counts for
14 papers · 1 filter
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…
Federated Causal Discovery From Interventions
Amin Abyaneh, Nino Scherrer, Patrick Schwab +3
Causal discovery serves a pivotal role in mitigating model uncertainty through recovering the underlying causal mechanisms among variables. In many practical domains, such as healt…
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…
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database
Arash Mehrjou, Ashkan Soleymani, Annika Buchholz +3
Federated learning (FL) has been proposed as a method to train a model on different units without exchanging data. This offers great opportunities in the healthcare sector, where l…
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations
Simon Bing, Andrea Dittadi, Stefan Bauer +1
The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understandi…
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson +4
In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that s…
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Sonali Parbhoo, Stefan Bauer, Patrick Schwab
Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economi…
Clinical Predictive Models for COVID-19: Systematic Study
Patrick Schwab, August DuMont Schütte, Benedikt Dietz +1
Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid human-to-…
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Patrick Schwab, Walter Karlen
Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and inte…
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab, Lorenz Linhardt, Stefan Bauer +2
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as health…
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
Patrick Schwab, Lorenz Linhardt, Walter Karlen
Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Coun…
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab, Emanuela Keller, Carl Muroi +3
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual a…
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab, Djordje Miladinovic, Walter Karlen
Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying dat…
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
Patrick Schwab, Gaetano Scebba, Jia Zhang +2
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for th…