Challenges of Using Text Classifiers for Causal Inference

By Zach Wood-Doughty, Ilya Shpitser, Mark Dredze. 2018

85 citations recorded by the source corpus.

Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.

Subfields: Artificial Intelligence, Natural Language Processing, Causal Inference & Discovery

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Citations in the corpus

Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, methodology): “, 2019), text as treatment (Fong and Grimmer, 2016; Egami et al.; Wood-Doughty et al., 2018; Tan et al., 2014), text as outcome (Egami et al.”
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2021, methodology): “One prominent approach to generate explanations is to exploit network artifacts, such as attention weights (Bahdanau et al., 2014), which are computed on the path to generating a prediction (e.g., Xu et al., 2015; Wang et al., 2016).”
A Survey on Causal Inference (2020, methodology): “Moreover, a series of methods have been proposed [44, 156, 169, 173] to handle the case when the treatment, confounder, or outcome contains textual data.”
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification (2021, unknown): “The tool of causality analysis has been successfully used in several areas, including explainable machine learning [40], fairness [24], natural language processing [61], reinforcement learning [20] and adversarial learning [22].”