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stat.ML2026
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…
stat.ML2022★ 16 cited
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…