  Unobserved confounding is common in many applications, making causal
inference from observational data challenging. As a remedy, causal sensitivity
analysis is an important tool to draw causal conclusions under unobserved
confounding with mathematical guarantees. In this paper, we propose NeuralCSA,
a neural framework for generalized causal sensitivity analysis. Unlike previous
work, our framework is compatible with (i) a large class of sensitivity models,
including the marginal sensitivity model, f-sensitivity models, and Rosenbaum's
sensitivity model; (ii) different treatment types (i.e., binary and
continuous); and (iii) different causal queries, including (conditional)
average treatment effects and simultaneous effects on multiple outcomes. The
generality of NeuralCSA is achieved by learning a latent distribution shift
that corresponds to a treatment intervention using two conditional normalizing
flows. We provide theoretical guarantees that NeuralCSA is able to infer valid
bounds on the causal query of interest and also demonstrate this empirically
using both simulated and real-world data.
