Building an Atlas of Social Experiments to Link Studies, Reconcile Conflicts, and Bridge Gaps
Jiawei Zhang, Honglin Bao, Pengda Wang, Alex Yan, Xiao Liu, James A. Evans
Social and behavioral science runs thousands of experiments each year, yet their findings rarely accumulate into a coherent map of what is known, what conflicts, and what remains missing. We introduce EXATLAS, a framework for turning an archive of experiments into an atlas: a structured map in which studies link, conflict, or leave bridgeable gaps. Given a target study, EXATLAS searches for prior studies that are locally close in treatment and outcome space and asks whether their observed effects can be composed to predict the target effect. This yields three cases. If composition succeeds and agrees with the observed result, EXATLAS links the target to consistent prior evidence. If composition succeeds but disagrees, EXATLAS reconciles the conflict and proposes candidate moderators or higher-level theories that could explain it. If the composition fails, EXATLAS proposes bridge experiments to close the gap. We provide an error bound for composition under local smoothness of the treatment-effect surface. On held-out targets certified as locally supported, EXATLAS recovers the direction of the reported causal effect in 98% of cases across two experimental domains, with an average Spearman correlation of rho = 0.770. Human evaluations further suggest that its proposed bridge experiments are plausible and exhibit connectedness, and that its conflict explanations are useful for theory generation. These results suggest that the archive of social experiments contains more latent structure than current practice extracts, and that making this structure explicit can guide both future theory and future experimentation.
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