author
Victor Chernozhukov
also written as V. Chernozhukov
10 papers here · 5 people have them
tags: econometrics: 9machine learning: 8statistics: 3causal inference: 2ai: 1
lists: stats.club
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Chernozhukov, V., Wüthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association.
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Chernozhukov, V., Wüthrich, K., & Zhu, Y. (2026). Debiasing and t -Tests for Synthetic Control Inference on Average Causal Effects. Journal of Political Economy.
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Klaassen, S., Teichert-Kluge, J., Bach, P., et al. (2024). DoubleMLDeep: Estimation of Causal Effects with Multimodal Data.
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Chernozhukov, V., Hong, H., & Tamer, E. (2007). Estimation and Confidence Regions for Parameter Sets in Econometric Models. Econometrica.
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Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-Dimensional Methods and Inference on Structural and Treatment Effects. Journal of Economic Perspectives.
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Belloni, A., Chernozhukov, V., & Hansen, C. (2014). Inference on Treatment Effects after Selection among High-Dimensional Controls. The Review of Economic Studies.
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Belloni, A., & Chernozhukov, V. (2011). ℓ1-penalized quantile regression in high-dimensional sparse models. The Annals of Statistics.
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Belloni, A., & Chernozhukov, V. (2013). Least squares after model selection in high-dimensional sparse models. Bernoulli.
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Chernozhukov, V., Chetverikov, D., Demirer, M., et al. (2018). Double/debiased Machine Learning for Treatment and Structural Parameters. The Econometrics Journal.
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Chernozhukov, V., Newey, W. K., & Singh, R. (2022). Automatic Debiased Machine Learning of Causal and Structural Effects. Econometrica.
None of these papers is tagged [machine learning].
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