48 citations · 209 across the 20 of their papers we have counts for
21 papers
Optimal data-driven hiring with equity for underrepresented groups
Yinchu Zhu, Ilya O. Ryzhov
We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is…
New possibilities in identification of binary choice models with fixed effects
Yinchu Zhu
We study the identification of binary choice models with fixed effects. We propose a condition called sign saturation and show that this condition is sufficient for identifying the…
Inference for Low-Rank Models
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has r…
Phase transition of the monotonicity assumption in learning local average treatment effects
Yinchu Zhu
We consider the setting in which a strong binary instrument is available for a binary treatment. The traditional LATE approach assumes the monotonicity condition stating that there…
Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)
Jelena Bradic, Yinchu Zhu
Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward. Statistics community responded with sl…
Can Two Forecasts Have the Same Conditional Expected Accuracy?
Yinchu Zhu, Allan Timmermann
The approach for testing equal predictive accuracy for pairs of forecasting models proposed by Giacomini and White (2006) assumes that the parameters of the underlying forecasting…
Minimax Semiparametric Learning With Approximate Sparsity
Jelena Bradic, Victor Chernozhukov, Whitney K. Newey +1
Estimating linear, mean-square continuous functionals is a pivotal challenge in statistics. In high-dimensional contexts, this estimation is often performed under the assumption of…
How well can we learn large factor models without assuming strong factors?
Yinchu Zhu
In this paper, we consider the problem of learning models with a latent factor structure. The focus is to find what is possible and what is impossible if the usual strong factor co…
Distributional conformal prediction
Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our…
Sparsity Double Robust Inference of Average Treatment Effects
Jelena Bradic, Stefan Wager, Yinchu Zhu
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to val…
Debiasing and -tests for synthetic control inference on average causal effects
Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a -fold cross-fitting procedure for bias…
Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
We study a panel data model with general heterogeneous effects where slopes are allowed to vary across both individuals and over time. The key dimension reduction assumption we emp…
Testability of high-dimensional linear models with non-sparse structures
Jelena Bradic, Jianqing Fan, Yinchu Zhu
Understanding statistical inference under possibly non-sparse high-dimensional models has gained much interest recently. For a given component of the regression coefficient, we sho…
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu
We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence…
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction…
Breaking the curse of dimensionality in regression
Yinchu Zhu, Jelena Bradic
Models with many signals, high-dimensional models, often impose structures on the signal strengths. The common assumption is that only a few signals are strong and most of the sign…
Comments on `High-dimensional simultaneous inference with the bootstrap'
Jelena Bradic, Yinchu Zhu
We provide comments on the article "High-dimensional simultaneous inference with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.
A projection pursuit framework for testing general high-dimensional hypothesis
Yinchu Zhu, Jelena Bradic
This article develops a framework for testing general hypothesis in high-dimensional models where the number of variables may far exceed the number of observations. Existing litera…
Two-sample testing in non-sparse high-dimensional linear models
Yinchu Zhu, Jelena Bradic
In analyzing high-dimensional models, sparsity of the model parameter is a common but often undesirable assumption. In this paper, we study the following two-sample testing problem…
Linear Hypothesis Testing in Dense High-Dimensional Linear Models
Yinchu Zhu, Jelena Bradic
We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model spa…
Significance testing in non-sparse high-dimensional linear models
Yinchu Zhu, Jelena Bradic
In high-dimensional linear models, the sparsity assumption is typically made, stating that most of the parameters are equal to zero. Under the sparsity assumption, estimation and,…