Causal Matching with Text Embeddings: A Case Study in Estimating the Causal Effects of Peer Review Policies (2023.findings-acl)
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| Challenge: | Using a method that uses text embeddings, we find that human judges prefer manuscript matches from our method in 70% of cases. |
| Approach: | They propose to use text embeddings to integrate a method to estimate the causal effects of peer review policies in publication venues that shift policies from single-blind to double-blinded. |
| Outcome: | The proposed method is compared with a widely-used method of stratified propensity score matching and a baseline of randomly selected matches. |
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
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| Challenge: | Existing text embeddings that predict direct causal links fail to capture other indirect causal links, leading to spurious correlations in downstream tasks. |
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Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
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| Challenge: | Existing methods for causal inference require strong assumptions about the data, meaning the data from which one *can* estimate valid causal effects is not representative of the actual target domain of interest. |
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| Challenge: | Recent advances in general purpose text embedders have been driven by training on synthetic training data. |
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On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach (2023.acl-short)
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| Challenge: | Word embeddings have been used to quantify biases in texts for years, but their statistical properties and advantages have not been studied. |
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