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Found 19 papers, 3 papers with code
Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information
This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available.
Semidiscrete optimal transport with unknown costs
Semidiscrete optimal transport is a challenging generalization of the classical transportation problem in linear programming.
New possibilities in identification of binary choice models with fixed effects
We provide a condition called sign saturation and show that this condition is sufficient for the identification of the model.
Optimal data-driven hiring with equity for underrepresented groups
We present a hiring policy that depends on the protected attribute functionally, but not statistically, and we prove that, among all possible fair policies, ours is optimal with respect to the firm's objective.
Phase transition of the monotonicity assumption in learning local average treatment effects
This boundary of phase transition is explicitly characterized in the case of binary outcomes.
Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)
Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward.
Minimax Semiparametric Learning With Approximate Sparsity
This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression.
Distributional conformal prediction
We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression.
Sparsity Double Robust Inference of Average Treatment Effects
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice.
Statistics Theory Econometrics Methodology Statistics Theory
A $t$-test for synthetic controls
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls.
Testability of high-dimensional linear models with non-sparse structures
Uniform non-testability identifies a collection of alternatives such that the power of any test, against any alternative in the group, is asymptotically at most equal to the nominal size.
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
We extend conformal inference to general settings that allow for time series data.
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
Breaking the curse of dimensionality in regression
In this article, we are interested in conducting large-scale inference in models that might have signals of mixed strengths.
Comments on `High-dimensional simultaneous inference with the bootstrap'
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
We propose a new inference method developed around the hypothesis adaptive projection pursuit framework, which solves the testing problems in the most general case.
Two-sample testing in non-sparse high-dimensional linear models
In analyzing high-dimensional models, sparsity of the model parameter is a common but often undesirable assumption.
Linear Hypothesis Testing in Dense High-Dimensional Linear Models
The test statistics are constructed in such a way that lack of sparsity in the original model parameter does not present a problem for the theoretical justification of our procedures.
Significance testing in non-sparse high-dimensional linear models
We show that existing inferential methods are sensitive to the sparsity assumption, and may, in turn, result in the severe lack of control of Type-I error.
