Text Based Confounders

Text-based confounders represent spurious correlations within textual data that bias analyses aiming to establish causal relationships. Current research focuses on developing methods to identify and mitigate these confounders, often employing machine learning techniques like double machine learning, variational autoencoders, and graph convolutional networks to learn disentangled representations or adjust for confounding effects. This work is crucial for improving the reliability of causal inference in diverse fields, ranging from healthcare (e.g., analyzing medical records) to social sciences (e.g., studying the impact of policies from textual data), where text data plays a significant role. Addressing text-based confounders enhances the validity and generalizability of findings derived from textual data.