Exploring Sparse Autoencoders in Text-Based Causal Confounding Adjustment

Abstract:In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in constructing text representations for adjustment: they must be sufficiently large and/or dense to preserve the confounding variables necessary for unbiased effect estimation, but sufficiently small and/or sparse to satisfy finite-sample overlap and yield low-variance estimates. To address this tradeoff, we turn to sparse autoencoders (SAEs), and propose a novel causal adjustment pipeline that iteratively selects a minimal set of SAE features via conditional independence tests. We find that SAE representations achieve better adjustments (lower bias and and higher coverage) than alternative representations in standard semi-synthetic evaluations with binary confounders, and their interpretability offers opportunities for falsification. We also introduce a more realistic semi-synthetic evaluation that uses multi-label data as the unobserved confounders and find off-the-shelf adjustment methods require increased investigation for these more complex settings. Code: this https URL

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.01322 [cs.CL]

(or arXiv:2609.01322v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.01322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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