This talk will discuss recent innovations in causal inference literature on the identification and estimation of causal effects from observational data in presence of endogeneity or equivalently unmeasured confounding bias. We will focus primarily on three recent developments: (i) The Proximal causal inference framework that leverages imperfect proxies of unmeasured confounders to remove hidden bias in observational analyses; (ii) Bespoke Instrumental variable framework that leverages a reference population in which a known intervention sets the treatment to generate a bespoke instrument tailored to account for endogeneity in a target population of interest; (iii) Invalid instrumental variable framework that leverages one or more invalid instruments to nevertheless correct for endogeneity bias without requiring that core instrumental variable assumptions hold. We view these new techniques as important strategies to relax the standard un-confoundedness assumptions commonly used in practice. Machine Learning tools implementing the methods and corresponding small bias guarantees will be described along with several empirical examples demonstrating the new methods in action.