16 citations · 34 across the 8 of their papers we have counts for
8 papers
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…
Algorithm-agnostic significance testing in supervised learning with multimodal data
Lucas Kook, Anton Rask Lundborg
Valid statistical inference is crucial for decision-making but difficult to obtain in supervised learning with multimodal data, e.g., combinations of clinical features, genomic dat…
Efficient adjustment for complex covariates: Gaining efficiency with DOPE
Alexander Mangulad Christgau, Anton Rask Lundborg, Niels Richard Hansen
Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data. Assuming a known graphical structure of the data generating…
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…
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…
The Projected Covariance Measure for assumption-lean variable significance testing
Anton Rask Lundborg, Ilmun Kim, Rajen D. Shah +1
Testing the significance of a variable or group of variables for predicting a response , given additional covariates , is a ubiquitous task in statistics. A simple but co…
Confounder Selection: Objectives and Approaches
F. Richard Guo, Anton Rask Lundborg, Qingyuan Zhao
Confounder selection is perhaps the most important step in the design of observational studies. A number of criteria, often with different objectives and approaches, have been prop…
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
Anton Rask Lundborg, Rajen D. Shah, Jonas Peters
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalise…