Conceptualizing Treatment Leakage in Text-based Causal Inference
This addresses a critical assumption in causal inference for social sciences and other fields using text data, but it is incremental as it builds on existing methods to handle a specific bias issue.
The paper tackles the problem of treatment leakage in text-based causal inference, where text contains information about treatment assignment, leading to post-treatment bias, and proposes text distillation as a pre-processing step to mitigate this bias, showing through simulation that it reduces bias in average treatment effect estimates.
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.