Computer Science / Artificial Intelligence

Reading Between the Lines: Deconfounding Causal Estimates using Text Embeddings and Deep Learning

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๐Ÿ“ Original Info

  • Title: Reading Between the Lines: Deconfounding Causal Estimates using Text Embeddings and Deep Learning
  • ArXiv ID: 2601.01511
  • Date: 2026-01-04
  • Authors: Ahmed Dawoud, Osama El-Shamy

๐Ÿ“ Abstract

Estimating causal treatment effects in observational settings is frequently compromised by selection bias arising from unobserved confounders. While traditional econometric methods struggle when these confounders are orthogonal to structured covariates, high-dimensional unstructured text often contains rich proxies for these latent variables. This study proposes a Neural Network-Enhanced Double Machine Learning (DML) framework designed to leverage text embeddings for causal identification. Using a rigorous synthetic benchmark, we demonstrate that unstructured text embeddings capture critical confounding information that is absent from structured tabular data. However, we show that standard tree-based DML estimators retain substantial bias (+24%) due to their inability to model the continuous topology of embedding manifolds. In contrast, our deep learning approach reduces bias to -0.86% with optimized architectures, effectively recovering the ground-truth causal parameter. These findings suggest that deep learning architectures are essential for satisfying the unconfoundedness assumption when conditioning on high-dimensional natural language data.

๐Ÿ“„ Full Content

...(๋ณธ๋ฌธ ๋‚ด์šฉ์ด ๊ธธ์–ด ์ƒ๋žต๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์‚ฌ์ดํŠธ์—์„œ ์ „๋ฌธ์„ ํ™•์ธํ•ด ์ฃผ์„ธ์š”.)

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