  Causal inference methods that control for text-based confounders are becoming
increasingly important in the social sciences and other disciplines where text
is readily available. However, these methods rely on a critical assumption that
there is no treatment leakage: that is, the text only contains information
about the confounder and no information about treatment assignment. When this
assumption does not hold, methods that control for text to adjust for
confounders face the problem of post-treatment (collider) bias. However, the
assumption that there is no treatment leakage may be unrealistic in real-world
situations involving text, as human language is rich and flexible. Language
appearing in a public policy document or health records may refer to the future
and the past simultaneously, and thereby reveal information about the treatment
assignment.
  In this article, we define the treatment-leakage problem, and discuss the
identification as well as the estimation challenges it raises. Second, we
delineate the conditions under which leakage can be addressed by removing the
treatment-related signal from the text in a pre-processing step we define as
text distillation. Lastly, using simulation, we show how treatment leakage
introduces a bias in estimates of the average treatment effect (ATE) and how
text distillation can mitigate this bias.
