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My bibliography Save this articleMatching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality
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Cited by:
- Jiaming Zeng & Michael F. Gensheimer & Daniel L. Rubin & Susan Athey & Ross D. Shachter, 2022.
"Uncovering interpretable potential confounders in electronic medical records,"
Nature Communications, Nature, vol. 13(1), pages 1-14, December.
- Zeng, Jiaming & Gensheimer, Michael F. & Rubin, Daniel L. & Athey, Susan & Schachter, Ross D., 2021. "Uncovering Interpretable Potential Confounders in Electronic Medical Records," Research Papers 3950, Stanford University, Graduate School of Business.
- Hao Chen & Dylan S. Small, 2022. "New multivariate tests for assessing covariate balance in matched observational studies," Biometrics, The International Biometric Society, vol. 78(1), pages 202-213, March.
- Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
- Henrika Langen, 2022. "The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach," Papers 2209.00409, arXiv.org, revised Sep 2023.
- Roman Senninger & Jens Blom‐Hansen, 2021. "Meet the critics: Analyzing the EU Commission's Regulatory Scrutiny Board through quantitative text analysis," Regulation & Governance, John Wiley & Sons, vol. 15(4), pages 1436-1453, October.
- Margaret E. Roberts & Brandon M. Stewart & Richard A. Nielsen, 2020. "Adjusting for Confounding with Text Matching," American Journal of Political Science, John Wiley & Sons, vol. 64(4), pages 887-903, October.
- Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
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