  Recent text-based causal methods attempt to mitigate confounding bias by
estimating proxies of confounding variables that are partially or imperfectly
measured from unstructured text data. These approaches, however, assume
analysts have supervised labels of the confounders given text for a subset of
instances, a constraint that is sometimes infeasible due to data privacy or
annotation costs. In this work, we address settings in which an important
confounding variable is completely unobserved. We propose a new causal
inference method that uses multiple instances of pre-treatment text data,
infers two proxies from two zero-shot models on the separate instances, and
applies these proxies in the proximal g-formula. We prove that our text-based
proxy method satisfies identification conditions required by the proximal
g-formula while other seemingly reasonable proposals do not. We evaluate our
method in synthetic and semi-synthetic settings and find that it produces
estimates with low bias. To address untestable assumptions associated with the
proximal g-formula, we further propose an odds ratio falsification heuristic.
This new combination of proximal causal inference and zero-shot classifiers
expands the set of text-specific causal methods available to practitioners.
