Simplified tutorial on doubly robust learning for causal inference.
problem Challenges in applying doubly robust methods due to complexity and software barriers.
method Combines propensity score and outcome modeling for robust causal inference.
result Makes doubly robust learning accessible through simplified methodology and practical examples.
Proposes DR-ACI for causal effect intervals with temporal dependence.
problem Causal effect intervals under temporal dependence.
method Doubly robust adaptive conformal inference (DR-ACI).
result Constructs prediction intervals for causal effects.
New methods combine machine learning with doubly robust estimators for better treatment effect estimation.
problem Estimating average treatment effects from observational data.
method Doubly robust methods using machine learning techniques.
result Machine learning improves the performance of doubly robust estimators.
Corrects mismatch in consistency of nuisance estimators for doubly robust methods.
problem Mismatch in consistency of nuisance estimators in doubly robust methods.
method Calibrated debiased machine learning (calibrated DML) with isotonic regression adjustment.
result Calibrated DML yields doubly robust asymptotic normality with slower convergence of nuisance estimators.
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy …
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
problem Improving semi-supervised learning performance with limited labeled data.
method Introduces doubly robust self-training, a method that combines labeled and pseudo-labeled data to balance between labeled-only and pseudo-labeled-only training.
result Demonstrates superior performance of doubly robust self-training on ImageNet and nuScenes datasets.
New estimators improve causal inference in machine learning studies.
problem Improving causal inference in machine learning models.
method Doubly-robust cross-fit estimators for average causal effect.
result Doubly-robust cross-fit estimators outperform other methods in simulations.
StableDR stabilizes doubly robust learning for biased recommendation data.
problem Data missing not at random in recommender systems.
method StableDR, a stabilized doubly robust learning approach.
result StableDR achieves bounded bias, variance, and generalization error.
The consistency of doubly robust estimators relies on consistent estimation of at least one of two nuisance regression parameters. In moderate to large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, n 1 / 2 n^{1/2} n 1/2 -consistency of doubly robust estimators is…
This paper investigates robust and efficient DR/RDR estimators for WATEs.
problem Lack of systematic investigation into robustness and efficiency conditions for WATE estimation.
method Proposes three RDR estimators using semiparametric efficient influence function and double/debiased machine learning.
result Demonstrates the practical relevance of the methods in medical and social sciences.
Doubly-stochastic normalization improves robustness to heteroskedastic noise.
problem Robustness to heteroskedastic noise in affinity matrix construction.
method Doubly-stochastic normalization of the Gaussian kernel.
result Doubly-stochastic normalization converges to clean matrix with rate m − 1 / 2 m^{-1/2} m − 1/2 under heteroskedastic noise. New method combines strengths of two PCL approaches without density ratio estimation.
problem Estimating causal functions in Proxy Causal Learning with unobserved confounders and proxies.
method Kernel-based doubly robust estimators combining treatment and outcome bridges, density ratio-free.
result Outperforms existing methods on PCL benchmarks, including a prior doubly robust method.
Proposes a robust method for counterfactual classification.
problem Decision-making under hypothetical scenarios.
method Doubly-robust nonparametric estimator for counterfactual classification.
result Robust against nuisance model misspecification, can attain fast n \sqrt{n} n rates. Proposes DR algorithms for distributionally robust off-policy evaluation and learning.
problem Sensitive to environment distribution shifts in offline observational data.
method Doubly robust and distributionally robust approaches for OPE/L.
result Achieves semiparametric efficiency and fast regret rate.
CPME embeds counterfactual outcomes in RKHS for flexible policy evaluation.
problem Estimating counterfactual policy outcomes for decision-making.
method Counterfactual Policy Mean Embedding (CPME) framework in RKHS, plug-in and doubly robust estimators, kernel test statistic.
result Doubly robust estimator improves convergence rates and asymptotic normality.
New estimator for causal effects in large datasets.
problem Unobserved confounding in large-scale data.
method Doubly robust estimator combining imputation, IPW, and cross-fitting.
result Error converges to Gaussian distribution at parametric rate.
New test for binary treatment effects using kernel methods.
problem Testing distributional effects of binary treatments.
method Kernel-based doubly-robust test, avoiding permutations.
result Valid type-I error with computational efficiency.
Proposes a robust algorithm for aligning large language models with human preferences.
problem Misspecification in preference models, reference policies, and reward functions.
method Doubly robust preference optimization algorithm.
result Superior and more robust performance compared to state-of-the-art algorithms.
Natural experiment dataset reveals inconsistent treatment effect estimators.
problem Inconsistent results from over 20 estimators on a new dataset.
method Created a benchmark to evaluate estimator accuracy, derived variance formula, introduced new estimator.
result Doubly robust estimators outperform others by orders of magnitude.
Improves SSL with doubly robust estimation of unlabeled class distribution.
problem Limited labeled data and long-tailed class distributions in unlabeled data.
method Explicitly estimate unlabeled class distribution using doubly robust estimator.
result Improves performance of SSL methods on unlabeled data.
New method improves robustness of double robust estimators under complete misspecification.
problem Improper performance of double robust estimators when all nuisance functions are misspecified.
method DR+ACC, an adaptive correction clipping method.
result DR+ACC ensures bounded error and maintains semiparametric efficiency.
New methods estimate policy value and gradients for deterministic policies from off-policy data.
problem Estimating policy value and gradients for deterministic policies from off-policy data.
method Proposed new doubly robust estimators based on kernelization approaches.
result Demonstrated a rate independent of horizon length for policy value and gradient estimation.
New estimator improves ATT estimation efficiency with external controls.
problem Reduced efficiency when incorporating external controls into ATT estimation.
method Proposes a novel doubly robust estimator for ATT that maintains higher efficiency than standard approaches.
result Demonstrates improved efficiency of the new estimator compared to standard approaches, even under model misspecification.
DR-MCTS improves decision quality and sample efficiency in complex environments.
problem Improving decision quality and sample efficiency in complex environments.
method Integrates Doubly Robust off-policy estimation into Monte Carlo Tree Search (MCTS).
result DR-MCTS achieves superior performance in Tic-Tac-Toe and VirtualHome tasks.
Proposes a robust estimator for RD designs.
problem Estimating treatment effects in RD designs.
method Doubly robust estimator combining two estimators.
result Enhances robustness of treatment effect estimators.
When training a machine learning model with observational data, it is often encountered that some values are systemically missing. Learning from the incomplete data in which the missingness depends on some covariates may lead to biased estimation of parameters and even harm the fairness of decision outcome. This paper …
New algorithm reduces regret in GLM bandits with tighter bounds.
problem Reducing regret in generalized linear contextual bandits.
method Double Doubly Robust (DDR) estimator for independence.
result First d \sqrt{d} d regret bound for GLM bandits. Paper proves optimality of doubly robust estimators for treatment effects.
problem Estimating treatment effects in causal inference.
method Structure-agnostic framework of statistical lower bounds, using non-parametric regression and classification oracles.
result Doubly robust estimators are statistically optimal for ATE and ATT.
New tests for distributional causal effects using improved kernel estimators.
problem Testing for higher-order moments and multidimensional outcomes affected by treatment.
method Improved kernel estimators based on doubly robust mean embeddings.
result New permutation-based tests for distributional causal effects with improved convergence rates.
Extends robust methods for causal inference, improving estimator performance.
problem Estimating causal effects in the presence of latent confounders.
method Minimax kernel machine learning for doubly robust functionals.
result Proposed method leads to robust and high-performance estimators.
Proposes a Doubly Robust mean-CVaR portfolio method to improve investment risk management.
problem Portfolio optimization challenges in unstable financial markets.
method Doubly Robust approach to mean-CVaR portfolio optimization.
result The proposed method outperforms traditional mean-variance optimization.
Proposes a neural network method to combine nonprobability and probability survey samples.
problem Combining nonprobability and probability survey samples for accurate population mean estimation.
method Uses a deep neural network to estimate sampling scores from nonprobability samples and combines them with probability sample information.
result Proposed estimators improve robustness to parametric propensity-score misspecification, especially for nonlinear selection mechanisms.
This study optimizes neural networks for doubly robust ATE estimation to balance bias and variance.
problem Balancing bias and variance in doubly robust estimators with neural networks.
method Investigates two neural network architectures and their hyperparameters in the presence of confounders and IVs.
result Optimal hyperparameters for neural networks reduce bias-variance tradeoff for ATE estimators.
We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central task is eval…
Proposes a robust estimator for high-dimensional data with heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects with many more regressors than observations.
method Doubly robust two-stage semiparametric difference-in-difference estimator using machine learning for propensity score estimation.
result Valid inference for heterogeneous treatment effects with bias correction procedures.
Data-driven Distributionally Robust Optimization (DD-DRO) via optimal transport has been shown to encompass a wide range of popular machine learning algorithms. The distributional uncertainty size is often shown to correspond to the regularization parameter. The type of regularization (e.g. the norm used to regularize)…
Estimates and tests treatment effects on entire outcome distributions.
problem Treatment effects on entire outcome distributions, not just averages.
method Proposes a novel estimand and doubly robust estimator, develops a test.
result First test with provably valid type 1 error guarantees in this setting.
A new estimator for evaluating policies in unknown environments.
problem Evaluating policies when both logging policy and value function are unknown.
method Doubly-Robust (DR) off-policy evaluation (OPE) estimator, DRUnknown, that estimates both the logging policy and value function.
result DRUnknown achieves the smallest asymptotic variance and is optimal when both models are correctly specified.
Paper tackles causal inference with partially labeled data, introducing robust methods.
problem Challenges in causal inference due to partially labeled datasets and potential bias.
method Decaying missing-at-random framework and BRSS estimator for doubly robust causal inference.
result Established asymptotic normality of BRSS estimator under decaying labeling propensity scores.
Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feas…
Robustly infers manifold density and geometry under high-dimensional noise.
problem Inaccurate kernel density estimation under high-dimensional noise.
method Doubly stochastic normalization of Gaussian kernel.
result Robust tools for density estimation, noise magnitude estimation, and distance approximation.
Optimally estimates a functional using nuisance function tuning and sample splitting.
problem Estimating optimal rates for a doubly robust functional.
method Combines nuisance function tuning and sample splitting strategies.
result Shows optimal rates of convergence for various estimators.
Paper proposes a new DR estimator for adaptive experiments with improved performance.
problem Improving policy evaluation in adaptive experiments with dependent samples.
method Adaptive-fitting variant of sample-splitting for non-Donsker nuisance estimators.
result Proposed DR estimator shows better performance than other estimators with dependent samples.
A new method for feature selection robust to noise and design variability.
problem Feature selection in high-dimensional regression under sampling variability and measurement error.
method Injects controlled additive noise into the design matrix, fits a base selector, and aggregates selection frequencies.
result Improved robustness compared to Stability Selection and standard base selectors.
The paper develops methods for causal inference from single-cell RNA sequencing data with multiple outcomes.
problem Causal inference from single-cell RNA sequencing data with multiple heterogeneous outcomes.
method Generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes.
result Demonstrates the use of semiparametric inferential results for estimating causal effects in genomics.
Q-Learner estimates ratio-based treatment effects without imposing parametric structures.
problem Estimating treatment effects as ratios in non-linear settings.
method Decomposes ratio-CATE into two classification tasks, using doubly robust augmentations.
result Q-Learner outperforms other methods in low-conversion and observational data settings.
Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. Most of the existing algorithms have regret proportional to a polynomial function of the context dimension, d d d . In many applications ho…
Paper proposes methods to reduce bias and variance in recommender systems.
problem Bias in recommender systems due to users' preferences.
method Proposes a principled approach to reduce bias and variance in DR methods, and a novel semi-parametric collaborative learning approach.
result The proposed methods outperform existing debiasing methods in both theory and experiments.