8 citations · 16 across the 5 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…