Given a sample of bids from independent auctions, this paper examines the question of inference on auction objects (like valuation distributions, welfare measures, etc) under weak assumptions on information. We leverage the re- cent contributions of Bergemann and Morris [2013] in the robust mechanism design literature that exploit the link between Bayesian Correlated Equilibria and Bayesian Nash Equilibria in incomplete information games, to construct an econometrics framework that is computationally feasible and robust to assump- tions about information. Checking whether a particular valuation distribution belongs to the identified set is as simple as determining whether a linear program (LP) is feasible. This is the key characteristic of our framework. A similar LP can be used to learn about various welfare measures and policy counterfactuals. For inference and to summarize statistical uncertainty, we propose novel finite sample methods using tail inequalities that are used to construct confidence sets on identified sets. Monte Carlo experiments show adequate finite sample properties. We illustrate our approach by applying our methods to a data set from search Ad auctions and to data from OCS auctions.