Extends double linear policy with time-varying weights and proves robust positive expectation.
problem Ensuring robustness in policy optimization with time-varying parameters.
method Employed a novel elementary symmetric polynomials characterization approach to prove robust positive expectation (RPE). Derived explicit expressions for expected cumulative gain-loss and variance.
result Proved the robust positive expectation property holds for the extended double linear policy.
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.
A new algorithm improves offline reinforcement learning robustness.
problem Finding optimal policies in perturbed environments from offline data.
method Doubly Pessimistic Model-based Policy Optimization (P^2MPO) framework.
result Proves sample efficiency with robust partial coverage data.
Tests validity of DML estimators without assumptions.
problem Validating DML estimators without making assumptions.
method Develops tests to falsify assumptions for DML estimators.
result Falsifies assumptions for DML estimators with non-trivial power.
New trading policies preserve robust gains in presence of transaction costs.
problem Maintaining robust gains in asset trading with transaction costs.
method Proposed double linear trading policies, analyzed with Monte Carlo simulations and historical data.
result Desired robust positive expected gain can be preserved under certain conditions.
New method for estimating parameters in inverse problems using double robustness.
problem Estimating parameters defined as linear functionals of solutions to linear inverse problems.
method Source condition double robust inference method that uses iterated Tikhonov regularized adversarial estimators.
result Asymptotic normality of the parameter of interest as long as either the primal or dual inverse problem is sufficiently well-posed.
Proposes fair and robust methods for estimating treatment effects.
problem Estimating treatment effects while maintaining fairness.
method Simple, nonparametric framework with fairness constraints.
result Estimators are double robust and characterize welfare trade-offs.
Paper combines machine learning and model averaging for robust parameter estimation.
problem Estimating structural parameters with partially unknown functional forms.
method Pairing double/debiased machine learning with stacking for model averaging.
result DDML with stacking is more robust to unknown functional forms than single learners.
Improves neural network performance with double regularization.
problem Challenges of mislabeled data in neural networks.
method Double regularization combining model complexity and observation reweighting.
result Stronger robustness and generalization against mislabeling.
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.
Double descent found in DRL, improving generalization with model capacity.
problem Generalization in over-parameterized DRL models.
method Actor-Critic framework, Policy Entropy metric.
result Policy Entropy significantly reduces as model capacity increases, indicating improved generalization.
Bayesian method for estimating ATE with robustness to model misspecification.
problem Estimating average treatment effects under unconfoundedness.
method Double robust Bayesian inference using adjusted prior and posterior distributions.
result Bayesian credible sets form asymptotically exact confidence intervals.
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 …
Direct learning framework for integrating multi-source causal data.
problem Conditional average treatment effects inference from heterogeneous data.
method Direct learning framework, double robustness, causal information-aware weighting function.
result Effective causal data fusion in both homogeneous and heterogeneous scenarios.
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. A new method improves estimation of COVID-19 vaccine effectiveness.
problem Estimating vaccine effectiveness under the test-negative design.
method A doubly robust estimator (TNDDR) using cross-fitting and machine learning.
result The TNDDR estimator is n \sqrt{n} n -consistent, asymptotically normal, and doubly robust. Label noise in adversarial training leads to robust overfitting, explained and mitigated.
problem Label noise in adversarial training causes robust overfitting.
method Proposed a method to automatically calibrate labels.
result Consistent performance improvements across various models and datasets.
Study combines SEM, OLS, and DML for robustness checks in survey-based research.
problem Stability of SEM findings under alternative estimation frameworks.
method Staged robustness analysis framework connecting SEM, OLS, and DML.
result Identifies stable and unstable relationships across SEM, OLS, and DML checks.
Gradient descent recovers low-rank matrices from corrupted measurements with double over-parameterization.
problem Robust recovery of low-rank matrices from grossly corrupted measurements.
method Gradient descent with discrepant learning rates for double over-parameterized models.
result Gradient descent with discrepant learning rates provably recovers the underlying matrix without prior knowledge on rank or sparsity.
Consider the case that one observes a single time-series, where at each time t one observes a data record O(t) involving treatment nodes A(t), possible covariates L(t) and an outcome node Y(t). The data record at time t carries information for an (potentially causal) effect of the treatment A(t) on the outcome Y(t), in…
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. This paper characterizes and designs loss functions for robust classification with abstention.
problem Ensuring robustness against adversarial attacks and knowing when to abstain from prediction.
method Proposes adversarial robust reject option loss and characterizes surrogates for calibration.
result Shifted Double Ramp Loss and Shifted Double Sigmoid Loss satisfy the calibration conditions.
The paper investigates how calibrating propensity scores improves DML estimates of average treatment effects.
problem Improving the accuracy of DML estimates in finite samples.
method Propensity score calibration within the Double/debiased machine learning framework.
result Calibrating propensity scores reduces the root mean squared error of DML estimates of average treatment effects in finite samples.
This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a single integral using the error function. This single integral is then computed with efficiently implemented numerical techniques. The perf…
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.
Proposes a robust method for predicting missing outcomes in covariate shift adaptation.
problem Predicting missing outcomes in test data with covariate shift.
method Doubly robust estimator for covariate shift adaptation via importance weighting, incorporating an additional estimator for the regression function.
result Shows robustness against density-ratio estimation errors, maintaining consistency if either estimator is consistent.
Overparametrized models are vulnerable to adversarial perturbations, affecting robust generalization.
problem Understanding how overparametrization impacts robustness in adversarial training.
method Analyzing random features regression models with a precise asymptotic formula.
result High overparametrization can hurt robust generalization in adversarially trained models.
Paper develops a new estimator for dynamic treatment effects in high-dimensional settings.
problem Time-varying confounding and model misspecification in estimating dynamic treatment effects.
method Sequential model doubly robust estimator with moment-targeting estimates.
result Root-N inference achieved under model misspecification, even with high-dimensional covariates.
Improved estimators for causal inference using cross-fitting and undersmoothing.
problem Estimating expected conditional covariance in causal inference.
method Double cross-fit doubly robust (DCDR) estimators with undersmoothing for non-smooth nuisance functions.
result DCDR estimators achieve n \sqrt{n} n -consistency and asymptotic normality under minimal conditions. In this work we revisit gradient regularization for adversarial robustness with some new ingredients. First, we derive new per-image theoretical robustness bounds based on local gradient information. These bounds strongly motivate input gradient regularization. Second, we implement a scaleable version of input gradient…
SHIFT improves robustness in estimating dose-response functions with heavy-tailed contamination.
problem Outliers bias estimates of average dose-response functions in heavy-tailed data.
method SHIFT combines cross-fit nuisance orthogonalization, Welsch-loss, and defensive OLS refit.
result SHIFT reduces RMSE from 1.03 to 0.33 on localized contamination test.
Proposes CCME framework for estimating heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects in complex distributions.
method Embeds conditional distributions into RKHS, develops meta-estimators for CCME.
result Establishes finite-sample convergence rates and double robustness for CCME estimators.
Proposes a scalable method for counterfactual prediction using machine learning.
problem De-bias causal estimators with high-dimensional data in observational studies.
method Uses entropy balancing to learn weights minimizing Jensen-Shannon divergence, leading to robust counterfactual predictions.
result Consistent causal estimation if either propensity score or outcome model is correctly specified.
This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised fea…
The paper addresses bias in survival analysis due to informative censoring.
problem Bias in treatment effect estimates due to informative censoring in survival analysis.
method Assumption-lean framework using partial identification to derive bounds on CATE.
result Proposes a meta-learner, SurvB-learner, to estimate bounds on CATE.
Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of decrease in the standard accuracy. To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize bet…
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.
Sharp bounds on ATE with unmeasured confounders, valid even when misspecified.
problem Bounding average treatment effects with unmeasured confounders.
method Distributionally robust optimization, double sharpness, double validity.
result Proposes estimators with robustness properties for valid bounds.
Improves robustness of propensity score estimators in challenging settings.
problem Limited overlap, small sample sizes, or unbalanced data.
method Extends calibration techniques for propensity score models, focusing on sample-splitting schemes.
result Calibration reduces variance and bias in inverse probability weighting and double/debiased machine learning frameworks.
The paper tackles fairness in data and algorithms, expanding on prior work.
problem Discrimination and disparate treatment in data and algorithms.
method Targeted learning for nonparametric inference of fairness in the data generating process.
result Derivation and validation of estimators for fairness metrics like demographic parity and equal opportunity.
New estimators for causal effects in DAGs with hidden variables, addressing computational and statistical challenges.
problem Estimating causal effects in DAGs with hidden variables beyond traditional criteria.
method Introduces novel one-step corrected plug-in and targeted minimum loss-based estimators for causal effects in DAGs with hidden variables.
result Root-n consistent causal effect estimates with desirable statistical properties.
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.
A new approach optimizes weights in DLP for better risk-adjusted performance.
problem Optimizing time-varying weights in Double Linear Policy (DLP) for better risk-adjusted performance.
method Stochastic Model Predictive Control (SMPC) framework to maximize risk-adjusted returns while enforcing constraints.
result Empirical results show improved risk-adjusted performance and drawdown control.
New methods improve estimation accuracy in noisy settings.
problem Estimating treatment effects in the presence of treatment noise.
method Developed new structure-agnostic cumulant estimators and practical procedures for higher-order robustness.
result Demonstrated that existing DML estimator is suboptimal for non-Gaussian treatment noise and introduced ACE procedures for improved accuracy.
We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check f…
We develop methods to approximate derivatives for causal inference problems using data.
problem Estimating causal effects from data when distributions are not known.
method Constructive algorithm approximating Gateaux derivatives via finite differencing.
result Derives conditions for finite-difference approximations to preserve statistical benefits.
New method for estimating mean in SS inference with selection bias and decaying overlap.
problem Estimating mean in SS inference with selection bias and decaying overlap.
method Double Robust Semi-Supervised (DRSS) mean estimator.
result Consistent estimation of mean with correct specification of outcome or propensity score model.
A theorem for debiasing machine learning with finite sample guarantees.
problem Calculating confidence intervals for machine learning functionals.
method Debiased machine learning based on bias correction and sample splitting.
result Nonasymptotic debiased machine learning theorem with finite sample guarantees.