When learning a causal model of a system, a key motivation is the use of that model for downstream decision-making. In this talk, I will take a decision-centric perspective on causal structure learning, focused on a simple setting that is amenable to careful statistical analysis. In particular, we study causal effect estimation via covariate adjustment, when the causal graph is unknown, all variables are discrete, and the non-descendants of treatment are given. \[ \] We propose an algorithm which searches for a data-dependent "approximate" adjustment set via conditional independence testing, and analyze the bias-variance tradeoff entailed by this procedure. We prove matching upper and lower bounds on omitted confounding bias in terms of small violations of conditional independence. Further, we provide a finite-sample bound on the complexity of correctly selecting an "approximate" adjustment set and of estimating the resulting adjustment functional, using results from the property testing literature. \[ \] We demonstrate our algorithm on synthetic and real-world data, outperforming methods which ignore structure learning or which perform structure learning separately from causal effect estimation. I conclude with some open questions at the intersection of structure learning and causal effect estimation.