4 papers
Identifying Causal Effects Using a Single Proxy Variable
Silvan Vollmer, Niklas Pfister, Sebastian Weichwald
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we assume that we observe a single…
Fast Estimation of Partial Dependence Functions using Trees
Jinyang Liu, Tessa Steensgaard, Marvin N. Wright +2
Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the…
Perturbation-based Effect Measures for Compositional Data
Anton Rask Lundborg, Niklas Pfister
Existing effect measures for compositional features are inadequate for many modern applications, for example, in microbiome research, since they display traits such as high-dimensi…
Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding
Shimeng Huang, Niklas Pfister, Jack Bowden
Observational genome-wide association studies are now widely used for causal inference in genetic epidemiology. To maintain privacy, such data is often only publicly available as s…