Harvard econometrician Jose Zubizarreta is developing new statistical methods for the extraction of causal inferences from large datasets. His methods flexibly adjust for covariates in observational studies while also yielding more stable causal estimates. For part of the research, Zubizarreta will investigate formal and theoretical properties of these methods. His team, however, based as it is at a medical school, will also work on specific applications. These require, for example, developing a new framework for the design and analysis of observational studies with discontinuities, or developing new methods that improve the degree of control (covariate balance) and statistical efficiency of randomized experiments that enhance their generalizability. Zubizarreta plans to produce five peer-reviewed papers on these topics. In addition, all software, code, and examples will be produced in an open source programming language and made freely available, together with documentation and sample data, to the academic community and the public.