Discovering causal relationships from observational data, particularly in the presence of latent variables, poses a challenging problem. While numerous effective methods have been proposed, it is often unnecessary and wasteful to find the global structures when our interest lies solely in the local structure of one target variable. Current local structure learning methods largely assume causal sufficiency, meaning that all the common causes of the measured variables are observed. This talk first show how to locally identify potential parent and child nodes of a target node within models that include latent variables. Specifically, we provide a principled method for determining whether a variable is a cause or non-cause of a target, based solely on the local structure rather than the entire graph. Next, we demonstrate how to locally estimate the causal effects of parent nodes on the target node. Theoretically, we demonstrate the correctness of our approach under the assumptions of faithfulness, and the accurate checking of independencies.