arXiv research

Optimally estimates a functional using nuisance function tuning and sample splitting.

New method for inference on strongly identified functionals even when nuisance functions are weakly identified.

DML addresses biases in machine learning by estimating nuisance functions.

Optimal first-order methods are shown to be fundamental limits in functional estimation.

Optimal tuning for estimating ECC in proportional asymptotics.

We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function. We focus on a class of parameters that have influence function which depends on two infinite dimensiona…

We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-condit…

Extends robust methods for causal inference, improving estimator performance.

Corrects mismatch in consistency of nuisance estimators for doubly robust methods.

New estimator optimizes black-box model errors in semiparametric estimation.

NURD improves model performance by distilling representations independent of nuisance variables.

Optimal CATE estimation with structured contrast functions using KRR.

New method improves robustness of double robust estimators under complete misspecification.

New method for unbinned, profiled unfolding in particle physics.

We propose the orthogonal random forest, an algorithm that combines Neyman-orthogonality to reduce sensitivity with respect to estimation error of nuisance parameters with generalized random forests (Athey et al., 2017)--a flexible non-parametric method for statistical estimation of conditional moment models using rand…

Paper introduces DRM for selecting robust CATE estimators.

We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data. The data is possibly subject to unmeasured confounding variables which, in a standard approach, must be adjusted for by estimating a nuisance function. Here we develop a method that eliminates the …

The joint PLDA model, is a generalization of PLDA where the nuisance variable is no longer considered independent across samples, but potentially shared (tied) across samples that correspond to the same nuisance condition. The original work considered a single nuisance condition, deriving the EM and scoring formulas fo…

The paper uses neural networks to estimate treatment effects even with many confounders.

Approach for selecting features by discarding nuisance and correlated ones.

Machine learning in high-energy physics faces challenges from nuisance parameters, which are reviewed and techniques to mitigate their impact are discussed.

A new framework evaluates HTE estimators using relative error.

Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.

We provide non-asymptotic excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target parameter depends on an unknown nuisance parameter that must be estimated from data. We analyze a two-stage sample splitting meta-algorithm that takes as input ar…

While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a fini…

Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization. We propose a framework for training robust neural networks that induces invariance to nuisances through learning to discover and separate predictive and nuisance factors of data. We pre…

New method uses MMD estimators to enforce model invariance with missing data.

New convergence guarantees for learning with unknown nuisance parameters.

AutoBayes automates Bayesian graph exploration for robust machine learning.

Proposes efficient estimators for weighted cumulative treatment effects in observational studies.

Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such representations becomes highly challenging in NLP tasks since the nuisance factor is entangled in a raw text. To our knowledge, a major issue is also…

A new method debiases multiple target parameters without IFs.

This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we es…

Improves statistical learning bounds with self-concordant losses.

Proposes a method to learn policies from offline data with reduced bias.

We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular f…

Bayesian method corrects bias in treatment effect estimation.

Proposes debiasing strategy for ill-posed regression problems.

Framework for multi-task learning with semiparametric models and nuisance parameters.

A new machine learning method handles nuisance parameters for better unfolding in particle physics.

Accuracy on in-distribution data correlates with out-of-distribution data when data is noisy or contains nuisance features.

Theory establishes optimal rates for estimating linear functionals without structural assumptions.

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions. Invar…

This guide simplifies high-probability regret bounds in empirical risk minimization.

New methods improve causal inference generalization using trial and observational data.

New method estimates and optimizes policy differences using orthogonal learning.

Improved estimators for causal inference using cross-fitting and undersmoothing.

CNN improves causal inference by controlling time-structured covariates.