4 papers · 1 filter
Machine-Learning-Powered Specification Testing in Linear Instrumental Variable Models
Cyrill Scheidegger, Malte Londschien, Peter Bühlmann
The linear instrumental variable (IV) model is widely used in observational studies, yet its validity hinges on strong assumptions. Classical specification tests such as the Sargan…
Covariate Adjustment for the Win Odds: Application to Cardiovascular Outcomes Trials
Cyrill Scheidegger, Simon Wandel, Tobias Mütze
Covariate adjustment can enhance precision and power in clinical trials, yet its application to the win odds remains unclear. The win odds is an extension of the win ratio that cou…
Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learni…
Spectral Deconfounding for High-Dimensional Sparse Additive Models
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
Many high-dimensional data sets suffer from hidden confounding which affects both the predictors and the response of interest. In such situations, standard regression methods or al…