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Unobserved confounding is common in many applications, making causalinference from observational data challenging. As a remedy, causal sensitivityanalysis is an important tool to draw causal conclusions under unobservedconfounding with mathematical guarantees. In this paper, we propose NeuralCSA,a neural framework for generalized causal sensitivity analysis. Unlike previouswork, our framework is compatible with (i) a large class of sensitivity models,including the marginal sensitivity model, f-sensitivity models, and Rosenbaum'ssensitivity model; (ii) different treatment types (i.e., binary andcontinuous); and (iii) different causal queries, including (conditional)average treatment effects and simultaneous effects on multiple outcomes. Thegenerality of \frameworkname is achieved by learning a latent distributionshift that corresponds to a treatment intervention using two conditionalnormalizing flows. We provide theoretical guarantees that NeuralCSA is able toinfer valid bounds on the causal query of interest and also demonstrate thisempirically using both simulated and real-world data.

A Neural Framework for Generalized Causal Sensitivity Analysis