Kernel Single Proxy Control for Deterministic Confounding

arXiv:2308.04585v3 Announce Type: replace-cross
Abstract: We consider the problem of causal effect estimation with an unobserved confounder, where we observe a proxy variable that is associated with the confounder. Although Proxy causal learning (PCL) uses two proxy variables to recover the true causal effect, we show that a single proxy variable is sufficient for causal estimation if the outcome is generated deterministically, generalizing Control Outcome Calibration Approach (COCA). We propose two kernel-based methods for this setting: the first based on …

abstract arxiv confounding control cs.lg kernel observe show stat.ml true type variables