arXiv research

Study improves statistical inference for CATEs using Lasso and DML.

Javanmard and Montanari propose a debiased estimator for high-dimensional regression.

We simplify debiasing of LASSO estimates for practical computation.

Ridge regression is revisited with debiasing and thresholding, offering advantages over Lasso.

Improved Lasso estimator speeds up variable selection.

Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference however, adaptive data collection induces memory and correlation in the samples, and poses significant challenge. We consider the high-dimensional linear regression, where the s…

The paper improves Lasso inference methods for survey data.

We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average "debiased" or "desparsified" lasso estimators. We show the approach converges at the same rate as the lasso as long as the dataset is not split across too many machines. We also extend the approach…

This paper introduces metrics for welfare analysis in dynamic models. We develop estimation and inference for these parameters even in the presence of a high-dimensional state space. Examples of welfare metrics include average welfare, average marginal welfare effects, and welfare decompositions into direct and indirec…

The paper analyzes LASSO penalization for high-dimensional Beta regression models.

New method controls FDR for sparse GLMs, identifying positive and negative relationships.

We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and…

We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model -- an actively studied topic in statistics and machine learning. In the noi…

In this study, we propose an automatic learning method for variables selection based on Lasso in epidemiology context. One of the aim of this approach is to overcome the pretreatment of experts in medicine and epidemiology on collected data. These pretreatment consist in recoding some variables and to choose some inter…

DWTS uses observational data to improve clinical trial efficiency.

Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance estimators. Comparing functional connectivity of subjects in two populations calls for comparing these estimated GGMs. Our goal is to identify differences in GGMs known to hav…

Study examines inference methods after variable selection in Cox models.

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the ti…

Develops a new method for uncertainty quantification in high-dimensional learning.

This paper introduces a novel online inference method for high-dimensional GLMs.

ADML combines debiased learning with data-driven model selection for efficient inference.

Two approaches to directly estimating Riesz representer are shown to be numerically equivalent under certain conditions.

DebiNet uses over-parameterized neural networks to improve linear model performance and debiasing.

The Lasso method is analyzed for high-dimensional regression with Gaussian designs, leading to new insights on its performance.

We consider linear regression in the high-dimensional regime where the number of observations $n$ is smaller than the number of parameters $p$. A very successful approach in this setting uses $\ell_1$-penalized least squares (a.k.a. the Lasso) to search for a subset of $s_0< n$ parameters that best explain the data, wh…

Performing statistical inference in high-dimension is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of high-dimensional estimators. Here, we consider linear regression in the high-dimensional regime $p\gg n$. In this context, we would like to perform in…

High-dimensional inference for sparse spectral precision matrices

Paper introduces structured sparsity estimators for Generalized Linear Models.

Method estimates dynamic treatment effects using machine learning and g-estimation.

Proposes a three-stage debiasing framework to improve out-of-distribution accuracy.

Paper develops PGMM framework for debiased inference on nonparametric IV estimators.

D3M debiases models by selectively removing problematic examples.

Paper debiases multiple word embedding biases simultaneously.

A new method debiases multiple target parameters without IFs.

We consider the problem of variable selection in high-dimensional statistical models where the goal is to report a set of variables, out of many predictors $X_1, \dotsc, X_p$, that are relevant to a response of interest. For linear high-dimensional model, where the number of parameters exceeds the number of samples $(p…

New method for group testing robust to errors in group membership specifications.

A self-supervised debiasing method using rank regularization mitigates spurious correlations in neural networks.

A new debiasing method for high-dimensional regression with applications to PCR.

This paper evaluates debiasing methods on word embeddings to reduce religious bias.

A debiasing method improves nonparametric regression's statistical properties.

Develops a direct debiased machine learning framework using Bregman divergence.

DKGM improves image quality by debiasing kernel-based models.

Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters. In this paper, we consider high-dimensional linear regression model in which a regularization procedure such as LASSO is applied to yield a sparse model. To establish a simultaneous pos…

Paper develops a distributed debiased estimator for sparse statistical inference.

COMMOD debiases models with minimal and interpretable changes.

Bayesian inference corrected for bias in high-dimensional models.

A theorem for debiasing machine learning with finite sample guarantees.

ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation