Neyman's framework evaluates personalized treatment rules using experiments.
problem Evaluating the efficacy of individualized treatment rules derived by machine learning.
method Neyman's repeated sampling framework applied to cross-fitted ITRs.
result Ex-post evaluation of ITRs can be more efficient than random assignment.
Develops a direct debiased machine learning framework using Bregman divergence.
problem Reduces bias in machine learning estimates of causal effects or structural models.
method Neyman targeted estimation and generalized Riesz regression using Bregman divergence.
result Improves estimation of parameters of interest in causal models.
Unified framework for debiased machine learning using Riesz representer and Bregman divergence.
problem Estimating causal and structural parameters in machine learning.
method Generalized Riesz regression for fitting Riesz representer via Bregman divergence minimization.
result Automatic covariate balancing and Neyman orthogonality properties for debiased estimation.
Unified framework for Bayes-optimal classifiers under group fairness.
problem Mitigating disparate impacts from algorithmic predictions in high-stakes decision-making.
method Unified framework based on Neyman-Pearson argument for deriving Bayes-optimal classifiers under group fairness constraints.
result Proposes FairBayes method that directly controls disparity and achieves optimal fairness-accuracy tradeoff.
Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.
problem Estimating unbiased average treatment effect in sequential experiments.
method Proposed adaptive designs with O ~ ( log T ) \widetilde{O}(\log T) O ( log T ) Neyman regret under boundedness assumptions and O ~ ( T ) \widetilde{O}(\sqrt{T}) O ( T ) multigroup Neyman regret in covariate-based settings. result Adaptive designs outperform non-adaptive designs in terms of Neyman regret, especially in covariate-based settings.
New bounds for Neyman-Pearson region using f f f -divergences.
problem Bounding the Neyman-Pearson region for hypothesis testing.
method Establishing novel lower and upper bounds using f f f -divergences. result Best possible lower bound for the Neyman-Pearson boundary using hockey-stick f f f -divergences. Neyman-Scott is a classic example of an estimation problem with a partially-consistent posterior, for which standard estimation methods tend to produce inconsistent results. Past attempts to create consistent estimators for Neyman-Scott have led to ad-hoc solutions, to estimators that do not satisfy representation inva…
Proposes a Bayesian framework for causal inference without explicit likelihood modeling.
problem Challenges in principled Bayesian inference for causal effects.
method Generalized Bayesian framework that places priors directly on causal estimands and updates using identification-driven loss functions.
result Yields generalized posteriors for causal effects with uncertainty quantification.
The paper tackles Neyman-Pearson classification control issues.
problem Neyman-Pearson classification's control constraint is hard to satisfy in finite samples.
method Developed refined learning procedures under two accuracy control strategies.
result Proposed methods achieve desired control levels in finite samples.
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introd…
A deep Neyman-Scott process uses Poisson processes for efficient inference in complex point processes.
problem Efficient inference in complex hierarchical point processes.
method Developed an efficient posterior sampling via Markov chain Monte Carlo for likelihood-based inference.
result More hidden Poisson processes improve likelihood fitting and event prediction.
Develops NPMC method for noisy labels, improving multiclass classification accuracy.
problem Asymmetric misclassification costs and label noise in multiclass classification.
method Empirical likelihood approach using exponential tilting density ratio model.
result Root n consistent and asymptotically normal estimators for clean labels and noise mechanism.
Optimal strategy found for identifying best arm in bandits with small gap.
problem Best arm identification in two-armed bandits with a fixed budget and small gap.
method Neyman allocation rule augmented with inverse probability weighting.
result Proposed strategy is asymptotically optimal when gap is small.
GNA optimally identifies the best arm with small gaps.
problem Best arm identification in fixed-budget settings.
method Generalized Neyman Allocation (GNA) for asymptotically locally minimax optimal BAI.
result GNA's worst-case bounds match the lower and upper bounds in the small-gap regime.
Adapts Neyman-Pearson classification for both source and target distribution shifts.
problem Minimizing errors while controlling both Type-I and Type-II errors under distribution shifts.
method Derives an adaptive procedure that guarantees improved error rates and adapts to uninformative sources.
result Automatic adaptation to uninformative sources avoids negative transfer.
DoubleML implements machine learning for causal inference in R.
problem Estimating causal effects in regression models with high-dimensional data.
method Double machine learning framework with Neyman orthogonality and sample splitting.
result Valid inference on causal parameters using machine learning methods.
Meta-learner estimates heterogeneous DiD effects robustly.
problem Estimating heterogeneous treatment effects in panel data with DiD.
method Doubly robust meta-learner for CATT, using convex risk minimization and auxiliary models.
result Superior performance over existing methods in empirical tests.
Paper proposes ClipSMT algorithm for better ATE estimation.
problem Adaptive estimation of Average Treatment Effect (ATE).
method ClipSMT algorithm for improved Neyman regret.
result Achieves exponential improvements in Neyman regret.
Optimal algorithm for identifying best-arm with minimal regret.
problem Identifying the best arm in two treatments with limited budget.
method Neyman allocation based on outcome standard deviations.
result Neyman allocation is minimax optimal for simple regret.
Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.
problem Asymmetric binary classification problems with unequal error severities.
method Develops TUBE-CS algorithm to bridge cost-sensitive and Neyman-Pearson paradigms.
result High-probability control of population type I error.
Develops adaptive framework for estimating survival effects with censoring.
problem Estimating causal effects in survival data with censoring.
method Derives semiparametric efficiency bound, proposes efficiency-optimal allocation policy, and develops Adaptive Survival Estimator (ASE).
result ASE achieves asymptotic normality via martingale central limit theorem and demonstrates efficiency gains over uniform randomization.
Develops variational inference for Neyman-Scott processes for faster sampling.
problem Slow mixing time in MCMC for posterior sampling in Neyman-Scott processes.
method Variational inference algorithm for Neyman-Scott processes, minimizing KL divergence.
result Achieves better prediction performance than MCMC with limited computational time.
New method for clustering tasks with heterogeneous data.
problem Clustered multitask learning with semiparametric and heterogeneous nuisances.
method Adaptive fused orthogonal estimator with Neyman-orthogonal losses and data-driven fusion penalties.
result Achieves exact clustering recovery and pooled parametric convergence rates.
Neyman-Pearson testing improves goodness of fit in detecting new physics.
problem Detecting small anomalies in data distributions.
method Employing Neyman-Pearson strategy with a rich parametrized family of models.
result Neyman-Pearson testing is more sensitive to small departures and unbiased towards specific anomalies.
Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.
problem Efficiently modeling and detecting clusters in spatiotemporal data.
method Adapting collapsed Gibbs sampling for Neyman-Scott processes via connections to mixture of finite mixture models.
result Demonstrated scalability and effectiveness on neural spike trains and document streams.
Characterizes distribution-free rates in unbalanced classification problems.
problem Minimizing error under two different distributions in unbalanced settings.
method Characterizes minimax rates over all pairs of distributions using a geometric condition.
result Identifies a dichotomy between hard and easy classes based on a three-points-separation condition.
DeepBlip estimates treatment effects over time using neural networks.
problem Estimating treatment effects over time with interpretable blip effects.
method DeepBlip uses a novel double optimization trick to enable simultaneous learning of blip functions with sequential neural networks.
result DeepBlip achieves state-of-the-art performance across various clinical datasets.
The paper argues for using Neyman orthogonal score for balancing in debiased machine learning.
problem Debiased machine learning requires a proper approach to balance covariates.
method The paper advocates for using Riesz regression with basis functions of X for balancing.
result Covariate balancing is only valid when the score-relevant regression error is a function of covariates alone.
New convergence guarantees for learning with unknown nuisance parameters.
problem Learning problems with unknown nuisance parameters.
method Stochastic gradient optimization with Neyman orthogonality and approximately orthogonalized updates.
result Stochastic gradient algorithms can converge under conditions of nuisance parameters.
Estimates treatment effects in randomized experiments with non-compliance.
problem Estimating distributional treatment effects in experiments with imperfect compliance.
method Proposes a regression-adjusted estimator based on distribution regression with Neyman-orthogonal moment conditions.
result Achieves semiparametric efficiency bound and demonstrates favorable performance in simulations and real data.
Framework sharpens causal effect estimates without external assumptions.
problem Estimating causal effects under unmeasured confounding.
method Information-theoretic divergence bounds, Neyman orthogonality, machine learning.
result Sharp partial identification of conditional causal effects from observational data.
New method corrects bias in density ratio estimation for missing data.
problem Missing data bias in density ratio estimation.
method Adapted KLIEP method (M-KLIEP) for MNAR data.
result M-KLIEP restores consistency and minimax optimality.
Develops algorithms for multi-class Neyman-Pearson classification with cost sensitivity.
problem Asymmetric misclassification costs in multi-class classification problems.
method Establishes connection with cost-sensitive learning, proposes two algorithms, extends NP oracle properties.
result Proposes algorithms with theoretical guarantees for multi-class Neyman-Pearson classification.
Paper proposes robust method to detect risk heterogeneity across ethnic groups.
problem Detecting risk heterogeneity across ethnic groups in ICU studies.
method Proposes a robust framework using Neyman orthogonality for inference.
result Demonstrates improved inferential stability and reduced bias compared to standard methods.
Optimal classification requires choosing the right group symmetries, contrary to intuition.
problem Improving binary classification performance by selecting appropriate group symmetries.
method Developed a theoretical framework for designing group equivariant neural networks.
result Optimal classification performance is achieved by selecting the appropriate subgroups of symmetries, not the largest equivariant groups.
Develops statistical inference for ML-discovered heterogeneous treatment effects.
problem ML algorithms may fail to accurately ascertain heterogeneous treatment effects in practical settings.
method Neyman's repeated sampling framework, dividing sample into groups, estimating average treatment effects, constructing confidence intervals.
result Valid methodology for estimating and testing heterogeneous treatment effects without relying on ML algorithm properties.
New method estimates hazard ratios without bias in observational studies.
problem Uninterpretable hazard ratios due to unspecified baseline hazard.
method Kernel-based machine learning to model risk set changes.
result Debiased maximum-likelihood estimators identify true hazard ratios.
A neural network for online NP classification with reduced complexity.
problem Online nonlinear Neyman-Pearson classification.
method Single hidden layer feedforward neural network (SLFN) initialized with random Fourier features (RFFs). Uses stochastic gradient descent for sequential learning.
result Expedited online adaptation and powerful nonlinear Neyman-Pearson modeling.
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new gene…
Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with …
Paper introduces GDR-learners for estimating potential outcomes from observational data.
problem Lack of theoretical property of general Neyman-orthogonality in deep generative models.
method Develops flexible GDR-learners based on various deep generative models.
result GDR-learners possess quasi-oracle efficiency and rate double robustness, asymptotically optimal.
Optimal selective classification using likelihood ratios improves model reliability.
problem Enhancing predictive model reliability by allowing uncertain predictions.
method Neyman--Pearson lemma applied to likelihood ratios for optimal selection.
result Neyman--Pearson-informed methods outperform existing baselines under covariate shifts.
New method improves efficiency analysis with big data.
problem Challenges in detecting inefficiency with big data.
method Post Double LASSO method using Neyman orthogonal moment conditions.
result Improved estimation of efficiency and inefficiency.
The paper enhances preference learning by incorporating response time data.
problem Lack of temporal information in user decision-making for reward model learning.
method Integrates response time alongside binary choice data using the EZ model and Neyman-orthogonal loss functions.
result Response time-augmented approach reduces error rates from exponential to polynomial scaling, improving sample efficiency.
Motivated by optimal investment problems in mathematical finance, we consider a variational problem of Neyman-Pearson type for law-invariant robust utility functionals and convex risk measures. Explicit solutions are found for quantile-based coherent risk measures and related utility functionals. Typically, these solut…
We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our…
SOFARI improves inference on multi-task learning latent factors.
problem Challenges in precise inference on multi-task learning latent factor matrices.
method High-dimensional manifold-based Neyman near-orthogonality inference on Stiefel manifold structure.
result Easy-to-use bias-corrected estimators for latent factor vectors and singular values with asymptotic normal distributions.
In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source. Previous work on this problem has assumed that the performance measure of interest is…