Graph Distance Based on Cause-Effect Estimands with Latents
This addresses the challenge of assessing progress in causal discovery for researchers, though it appears incremental as it builds on existing identification methods.
The paper tackles the problem of evaluating causal discovery methods under latent confounding by proposing a graph distance measure for ADMGs based on cause-effect estimands, analyzing its behavior under perturbations and comparing it to existing metrics.
Causal discovery aims to recover graphs that represent causal relations among given variables from observations, and new methods are constantly being proposed. Increasingly, the community raises questions about how much progress is made, because properly evaluating discovered graphs remains notoriously difficult, particularly under latent confounding. We propose a graph distance measure for acyclic directed mixed graphs (ADMGs) based on the downstream task of cause-effect estimation under unobserved confounding. Our approach uses identification via fixing and a symbolic verifier to quantify how graph differences distort cause-effect estimands for different treatment-outcome pairs. We analyze the behavior of the measure under different graph perturbations and compare it against existing distance metrics.