  A fundamental issue in causal inference for Big Observational Data is
confounding due to covariate imbalances between treatment groups. This can be
addressed by designing the data prior to analysis. Existing design methods,
developed for traditional observational studies with single designers, can
yield unsatisfactory designs with suboptimum covariate balance for Big
Observational Data due to their inability to accommodate the massive
dimensionality, heterogeneity, and volume of the Big Data. We propose a new
framework for the distributed design of Big Observational Data amongst
collaborative designers. Our framework first assigns subsets of the
high-dimensional and heterogeneous covariates to multiple designers. The
designers then summarize their covariates into lower-dimensional quantities,
share their summaries with the others, and design the study in parallel based
on their assigned covariates and the summaries they receive. The final design
is selected by comparing balance measures for all covariates across the
candidates. We perform simulation studies and analyze datasets from the 2016
Atlantic Causal Inference Conference Data Challenge to demonstrate the
flexibility and power of our framework for constructing designs with good
covariate balance from Big Observational Data.
