Spectral Representations for Accurate Causal Uncertainty Quantification
with Gaussian Processes
Spectral Representations for Accurate Causal Uncertainty Quantification
with Gaussian Processes
Accurate uncertainty quantification for causal effects is essential for robust decision making in complex systems, but remains challenging in non-parametric settings. One promising framework represents conditional distributions in a reproducing kernel Hilbert space and places Gaussian process priors on them to infer posteriors on causal effects, but requires restrictive nuclear …