  Drawing causal conclusions from observational data requires making
assumptions about the true data-generating process. Causal inference research
typically considers low-dimensional data, such as categorical or numerical
fields in structured medical records. High-dimensional and unstructured data
such as natural language complicates the evaluation of causal inference
methods; such evaluations rely on synthetic datasets with known causal effects.
Models for natural language generation have been widely studied and perform
well empirically. However, existing methods not immediately applicable to
producing synthetic datasets for causal evaluations, as they do not allow for
quantifying a causal effect on the text itself. In this work, we develop a
framework for adapting existing generation models to produce synthetic text
datasets with known causal effects. We use this framework to perform an
empirical comparison of four recently-proposed methods for estimating causal
effects from text data. We release our code and synthetic datasets.
