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Joined 2026-09-21 Message
Incoming links: Victor Chernozhukov
Alexandre Belloni 2026-11-05
Alexandre Belloni works across optimization, econometrics and high-dimensional statistics. His Chicago Booth profile describes research connecting statistical inference with decisions under limited information.[ref]
With Victor Chernozhukov and Lie Wang, he formulated the Square-root Lasso as a convex conic optimization problem. This connects a statistically pivotal tuning rule with algorithms for convex optimization, giving a computable sparse regression estimator without a preliminary estimate of noise scale.[ref]
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming by Alexandre Belloni and others. Abstract and section 1, optimization formulation
By Victor Chernozhukov and others.
Version-specific title and byline verified on PDF page 1; arXiv:1608.00060v6 dated 12 December 2017. Cited passages: Section 2.1, Neyman orthogonality; Section 3, Definitions 3.1–3.2, cross-fitting/DML constructions and nuisance-rate conditions; Section 5.1, equation (5.3) and Theorem 5.1, orthogonal average-treatment-effect score and asymptotic inference. The later arXiv v7 changes Structural to Causal in the title; this card refers specifically to v6.
Lie Wang 2026-11-05
Lie Wang coauthored the Square-root Lasso paper with Alexandre Belloni and Victor Chernozhukov. Their estimator combines a root-mean-square residual loss with an penalty. Its tuning rule avoids requiring advance knowledge of the noise standard deviation, making sparse linear regression feasible when the number of candidate predictors exceeds the number of observations.[ref]
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming by Alexandre Belloni and others. Section 1, equations 8–11; exact author byline
Victor Chernozhukov 2026-11-05
Victor Chernozhukov works on econometrics and statistical inference with machine learning. With Alexandre Belloni and Lie Wang, he developed the Square-root Lasso, whose penalty calibration does not require knowing the noise scale.[ref]
He also coauthored work on double machine learning. Predicting an outcome accurately does not by itself estimate a causal effect accurately: errors in fitted nuisance functions can bias the target parameter. An orthogonal estimating equation reduces first-order sensitivity to those errors, while cross-fitting separates training observations from score evaluation. Valid confidence intervals still require the paper's identification and convergence assumptions.[ref]