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Challenges of Using Text Classifiers for Causal Inference.

, , и . EMNLP, стр. 4586-4598. Association for Computational Linguistics, (2018)

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Using Noisy Self-Reports to Predict Twitter User Demographics., , , и . SocialNLP@NAACL, стр. 123-137. Association for Computational Linguistics, (2021)DoubleLingo: Causal Estimation with Large Language Models., и . NAACL (Short Papers), стр. 799-807. Association for Computational Linguistics, (2024)Proxy Model Explanations for Time Series RNNs., , и . ICMLA, стр. 698-703. IEEE, (2021)How Does Twitter User Behavior Vary Across Demographic Groups?, , , и . NLP+CSS@ACL, стр. 83-89. Association for Computational Linguistics, (2017)Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status., и . CoRR, (2024)Segment Anything Model is a Good Teacher for Local Feature Learning., , , и . CoRR, (2023)Audio-Journey: Open Domain Latent Diffusion Based Text-To-Audio Generation., , , , , и . ICASSP, стр. 6960-6964. IEEE, (2024)Predicting Twitter User Demographics from Names Alone., , , и . PEOPLES@NAACL-HTL, стр. 105-111. Association for Computational Linguistics, (2018)Challenges of Using Text Classifiers for Causal Inference., , и . EMNLP, стр. 4586-4598. Association for Computational Linguistics, (2018)Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond., , , , , , , , , и 3 other автор(ы). CoRR, (2021)