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Estimating the effects of continuous-valued interventions from observationaldata is a critically important task for climate science, healthcare, andeconomics. Recent work focuses on designing neural network architectures andregularization functions to allow for scalable estimation of average andindividual-level dose-response curves from high-dimensional, large-sample data.Such methodologies assume ignorability (observation of all confoundingvariables) and positivity (observation of all treatment levels for everycovariate value describing a set of units), assumptions problematic in thecontinuous treatment regime. Scalable sensitivity and uncertainty analyses tounderstand the ignorance induced in causal estimates when these assumptions arerelaxed are less studied. Here, we develop a continuous treatment-effectmarginal sensitivity model (CMSM) and derive bounds that agree with theobserved data and a researcher-defined level of hidden confounding. Weintroduce a scalable algorithm and uncertainty-aware deep models to derive andestimate these bounds for high-dimensional, large-sample observational data. Wework in concert with climate scientists interested in the climatologicalimpacts of human emissions on cloud properties using satellite observationsfrom the past 15 years. This problem is known to be complicated by manyunobserved confounders.

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions