The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text

2026-07-28

AI Summary

This study addresses the challenge of estimating causal effects of linguistic attributes from observational text, where treatment variables often co-occur with confounders within the same document, leading conventional representation learning methods to inadvertently encode treatment signals and thereby violate the overlap assumption. The work formally characterizes this issue as “representation-induced overlap failure” and, focusing on settings where treatments are explicitly encoded by specific lexical items, proposes a pre-representation masking strategy—either deleting or replacing treatment-indicative words—to remove such signals. Theoretical analysis demonstrates that deletion masking preserves overlap under bag-of-words and topic models, while replacement masking naturally extends to large language models. Experiments show that the proposed approach substantially improves overlap diagnostics, stabilizes causal effect estimates, and significantly reduces bias.

Abstract

Estimating causal effects of linguistic properties from observational text is difficult because the same document can contain both the treatment of interest and the non-treatment textual attributes needed for adjustment. Existing approaches often learn representations from the full text to capture latent confounding, but when treatment status is itself encoded by words in the text, these representations can directly encode treatment. This creates a confounder trap: richer representations can make treated and control documents separable, inducing overlap violations even when the underlying causal problem satisfies overlap. We study latent text treatments that are encoded through lexicons or other treatment-defining lexical information, and propose masking-based adjustment representations that remove this lexical treatment signal before representation learning. We formalize representation-induced overlap failure, prove that deletion masking preserves overlap for bag-of-words/topic-model representations, and characterize replacement masking as a natural relaxation for large language models that hides treatment-defining tokens while preserving word order and context. Across simulations, masking improves overlap diagnostics, stabilizes treatment effect estimates, and reduces bias relative to adjustment methods that learn from the unmasked text.

Problem

• causal inference
• text representation
• confounding
• overlap violation
• treatment encoding

Innovation

• confounder trap
• masking-based adjustment
• representation-induced overlap failure
• causal inference with text
• treatment-encoding representations