Victor Chernozhukov Mit Economics "generic machine learning inference on heterogeneous treatment effects". Victor chernozhukov. professor, department of economics center for statistics, mit, usa; international fellow, ucl cemmap, united kingdom; professor by courtesy, new economic school, russian federation; ph.d. 2000, stanford, economics; m.s. 1997, university of illinois at urbana champaign, statistics; curriculum vitae.
Victor Chernozhukov Mit Economics Co editor of the econometrics journal and an action editor of the journal of machine learning research. elected fellow of the american academy of arts and sciences, econometric society, and institute of mathematical statistics. inaugural moderator for the economics section of arxiv.org launched in 2017. Victor chernozhukov ford international professor of economics research fields. econometrics contact information. office phone 617 253 4767 email address [email protected] office e52 524. assistant name claire bartolone links personal website. department of economics massachusetts institute of technology. We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. these key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. View a pdf of the paper titled double debiased machine learning for treatment and causal parameters, by victor chernozhukov and 6 other authors.
Victor Chernozhukov Idss We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. these key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. View a pdf of the paper titled double debiased machine learning for treatment and causal parameters, by victor chernozhukov and 6 other authors. The python and r packages doubleml provide an implementation of the double debiased machine learning framework of chernozhukov et al. (2018). the python package is built on top of scikit learn (pedregosa et al., 2011) and the r package on top of mlr3 and the mlr3 ecosystem (lang et al., 2019). Victor chernozhukov (Виктор Викторович Черножуков) is a russian american statistician and economist currently at massachusetts institute of technology. his current research focuses on mathematical statistics and machine learning for causal structural models in high dimensional environments. Victor chernozhukov works in econometrics and mathematical statistics, with much of recent work focusing on the quantification of uncertainty in very high dimensional models. he is a fellow of the econometric society and a recipient of the alfred p. sloan research fellowship and the arnold zellner award. Chernozhukov's recent work solved an important, long standing problem in regression, namely inference on coefficients of interest after selection of covariates. he developed machine learning methods for causal inference and treatment effect evaluation with high dimensional data.
Victor Chernov Ph D Aerospace Engineering Technion Israel The python and r packages doubleml provide an implementation of the double debiased machine learning framework of chernozhukov et al. (2018). the python package is built on top of scikit learn (pedregosa et al., 2011) and the r package on top of mlr3 and the mlr3 ecosystem (lang et al., 2019). Victor chernozhukov (Виктор Викторович Черножуков) is a russian american statistician and economist currently at massachusetts institute of technology. his current research focuses on mathematical statistics and machine learning for causal structural models in high dimensional environments. Victor chernozhukov works in econometrics and mathematical statistics, with much of recent work focusing on the quantification of uncertainty in very high dimensional models. he is a fellow of the econometric society and a recipient of the alfred p. sloan research fellowship and the arnold zellner award. Chernozhukov's recent work solved an important, long standing problem in regression, namely inference on coefficients of interest after selection of covariates. he developed machine learning methods for causal inference and treatment effect evaluation with high dimensional data.
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