Conceptualizing Treatment Leakage in Text-based Causal Inference

Adel Daoud, Connor Jerzak, Richard Johansson


Abstract
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.
Anthology ID:
2022.naacl-main.413
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5638–5645
Language:
URL:
https://aclanthology.org/2022.naacl-main.413
DOI:
10.18653/v1/2022.naacl-main.413
Bibkey:
Cite (ACL):
Adel Daoud, Connor Jerzak, and Richard Johansson. 2022. Conceptualizing Treatment Leakage in Text-based Causal Inference. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5638–5645, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Conceptualizing Treatment Leakage in Text-based Causal Inference (Daoud et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.413.pdf