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This paper explores the use of unstructured, multimodal data, namely text and images, in causal inference and treatment effect estimation.
Simultaneous analysis of Lasso and Dantzig selector
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Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J · 2018
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Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., and Stewart, B. M · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Pytorch image models
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Veitch, V., Sridhar, D., and Blei, D · 2020
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records
Masukawa, K., Aoyama, M., Yokota, S., Nakamura, J., Ishida, R., and Nakayama, M · 2022
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Orthogonal statistical learning
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Dive into Deep Learning
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Hyperparameter tuning for causal inference with double machine learning: A simulation study
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