Proximal Causal Inference With Text Data
Jacob M. Chen Rohit Bhattacharya Katherine A. Keith
Recent text-based causal methods attempt to mitigate confounding bias by
including unstructured text data as proxies of confounding variables that are
partially or imperfectly measured. These approaches assume analysts have
supervised labels of the confounders given text for a subset of instances, a
constraint that is not always feasible due to data privacy or cost. Here, we
address settings in which an important confounding variable is completely
unobserved. We propose a new causal inference method that splits pre-treatment
text data, infers two proxies from two zero-shot models on the separate splits,
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. This combination of proximal causal inference
and zero-shot classifiers is novel (to our knowledge) and expands the set of
text-specific causal methods available to practitioners.