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stat.ME2026
Outcome-adapted Automatic Debiased Machine Learning
Asger Waagepetersen, Asbjørn Risom, Niels Richard Hansen +1
Parameters of interest in causal inference, such as treatment or policy effects, can often be expressed as linear functionals of an outcome regression function. Automatic debiased…
stat.ME2023
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…
stat.ME2023
Model-based causal feature selection for general response types
Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg +2
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal featur…