16 citations · 17 across the 6 of their papers we have counts for
6 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 this work, we assume that we observe a single, potentially multi-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…
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…
Instrumental Variable Estimation of Distributional Causal Effects
Lucas Kook, Niklas Pfister
Estimating the causal effect of a treatment on the entire response distribution is an important yet challenging task. For instance, one might be interested in how a pension plan af…
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…
Supervised Learning and Model Analysis with Compositional Data
Shimeng Huang, Elisabeth Ailer, Niki Kilbertus +1
The compositionality and sparsity of high-throughput sequencing data poses a challenge for regression and classification. However, in microbiome research in particular, conditional…