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Found 6 papers, 1 papers with code
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relationships.
Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding
We prove under these assumptions, a na\"ive semiparametric regression of $\mathbf{Y}$ on $\mathbf{A}$ is asymptotically unbiased.
How to Make Causal Inferences Using Texts
We argue that nearly all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation.
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