Abstract: The estimation of causal effects is increasingly relevant in different applied fields. In this work we consider a causal inference problem in the presence of interference. Our focus is on observational studies where interference across units is governed by a known network interference. However, the radius (and intensity) of interference is unknown and can be dependent on the observed treatment assignments in the relevant subnetwork.  We study causal estimators for average direct treatment effect given the network interference.