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.
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 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.
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.
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.
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.
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.
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.
The paper develops methods for causal function estimation and inference with multiway clustered data.
problem Estimation and inference for causal functions under multiway clustering.
method Two-step procedure using machine learning for nuisance parameters and projection onto basis functions.
result Rejects the null hypothesis of uniformly zero effects and reveals heterogeneous treatment effects.
ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.
problem Improving debiased machine learning and policy effects estimation.
method Score matching and Riesz representer estimation.
result Estimates policy path for continuous treatments, improving interpretability.
New LT-O-learners improve HLTE estimation with low overlap.
problem Challenges in estimating heterogeneous long-term treatment effects due to limited overlap.
method Introduces LT-O-learners that use custom overlap weights to downweight low-overlap samples.
result LT-O-learners provide robust HLTE estimates with lower variance in low-overlap regimes.
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.
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…
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.
A new meta-learner improves prediction of individualized outcomes in sequential decisions.
problem Predicting individualized outcomes over long horizons in sequential decision-making.
method Developed a novel meta-learner called DRQ-learner with theoretical guarantees of orthogonality and quasi-oracle efficiency.
result DRQ-learner achieves quasi-oracle efficiency, doubly robustness, and Neyman-orthogonality.
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.
Novel method to quantify aleatoric uncertainty of treatment effects from observational data.
problem Understanding randomness in treatment effects for medical treatments.
method Partial identification and Neyman-orthogonality to quantify aleatoric uncertainty.
result Developed a novel orthogonal learner (AU-learner) for quantifying aleatoric uncertainty.
Double machine learning provides n \sqrt{n} n -consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an n − 1 / 4 n^{-1/4} n − 1/4 rate. The key is to employ Neyman-orthogonal moment equations which are first-order insensitive to perturbations in the nuisance param…
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.
Paper develops efficient DML estimators for multiway clustered data without cross-fitting.
problem Efficient inference in models with multiway clustered dependence.
method Neyman-orthogonal moment conditions combined with localisation-based empirical process approach.
result Valid inference achieved without cross-fitting, showing debiased GMM estimators are asymptotically linear and normal.
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.
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.
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.
The paper uses double machine learning to estimate dynamic treatment effects robustly.
problem Estimating causal effects of dynamic treatments with time-varying covariates.
method Double machine learning with Neyman-orthogonal score functions for robustness.
result Asymptotic normality and n \sqrt{n} n -consistency of the estimators under specific conditions. 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.
Proposes a new method for analyzing multimodal neuroimaging data.
problem Combining interpretability and flexibility in multimodal data analysis.
method Orthogonalized kernel debiased machine learning approach.
result Established consistency and asymptotic normality of the estimated primary parameter.
Method estimates heterogeneous causal effects on networks using orthogonal learning.
problem Challenges in estimating causal effects on networks due to treatment effects on both treated and neighbors, and network homophily.
method Two-stage orthogonal learning framework: first stage uses graph neural networks for nuisance components, second stage residualizes and interpretable attention-based model for causal effects.
result Improves heterogeneous effect estimation and supports interpretable analyses.
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.
Proposes a method to correct for covariate shift in meta-analysis of randomized trials.
problem Invalidation of standard IPD meta-analysis due to covariate shift across studies.
method Placebo-anchored transport framework that treats source-trial outcomes as proxy signals and target-trial placebo outcomes as gold labels.
result Yields target-identified effect estimates in connected targets and a principled screen--then--transport procedure in disconnected targets.
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…
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. This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample s…
The paper develops methods to estimate treatment effects in sample selection models.
problem Evaluation of treatments when outcomes are only observed for a subpopulation due to sample selection or attrition.
method Combines selection-on-observables and instrumental variable assumptions with double machine learning for treatment evaluation.
result Proposed estimators are asymptotically normal and root-n consistent.
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…
Automatic debiasing for causal and policy effects using Neural Nets and Random Forests.
problem Estimating causal and policy effects from high-dimensional or non-parametric regression functions.
method Automatic learning of Riesz representation using Neural Nets and Random Forests.
result Automatic debiasing method performs well compared to state-of-the-art algorithms.
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…
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.
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.
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.
The paper develops statistical inference methods for SHAP values.
problem Lack of statistical inference for SHAP values in model-agnostic feature importance.
method Semi-parametric approach using U-statistics and Neyman orthogonal scores for functionals of nested regressions.
result Asymptotically normal estimates of the pth powers of SHAP values for various p.
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.
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.
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…
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.