16 citations · 17 across the 5 of their papers we have counts for
7 papers
Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach
Yiwen Qiu, Francesco Montagna, Shimeng Huang +1
Selection bias is a difficult yet widespread problem in causal discovery, occurring whenever non-random selection processes lead to data that is not representative of the underlyin…
Towards a holistic understanding of Selection Bias for Causal Effect Identification
Yiwen Qiu, Filip Kovačević, Shimeng Huang +2
Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher soc…
Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
Shimeng Huang, Matthew Robinson, Francesco Locatello
Mendelian Randomization (MR) is a prominent observational epidemiological research method designed to address unobserved confounding when estimating causal effects. However, core a…
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
Dingling Yao, Shimeng Huang, Riccardo Cadei +2
Causal reasoning and discovery, two fundamental tasks of causal analysis, often face challenges in applications due to the complexity, noisiness, and high-dimensionality of real-wo…
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…
Causal Change Point Detection and Localization
Shimeng Huang, Jonas Peters, Niklas Pfister
Detecting and localizing change points in sequential data is of interest in many areas of application. Various notions of change points have been proposed, such as changes in mean,…
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…