New method offsets DML's error-compounding issue and provides more stable causal parameter estimates.
problem Estimating ATE from observational data with robustness and stability.
method Robust Causal Learning (RCL) method to offset DML's deficiencies.
result RCL estimators are more stable and perform better than DML and traditional 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.
DML addresses biases in machine learning by estimating nuisance functions.
problem Bias in machine learning models due to nuisance functions.
method Double/Debiased Machine Learning (DML) approach to reduce biases.
result DML allows flexible estimation of nuisance functions without auxiliary assumptions.
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.
Proposes RCL method to improve ATE estimation from observational data.
problem Error-compounding issue and extreme estimates in DML estimators.
method Robust Causal Learning (RCL) method to offset DML deficiencies.
result RCL estimators are more stable and perform better than DML estimators.
DML-CMR estimator reduces bias in CMR problems using deep neural networks.
problem Solving conditional moment restrictions with deep neural networks.
method Double/debiased machine learning framework for unbiased estimation.
result Achieves minimax optimal convergence rate of O ( N − 1 / 2 ) O(N^{-1/2}) O ( N − 1/2 ) . DML-IV improves IV regression for learning decision policies by reducing bias.
problem Spurious correlations in offline datasets caused by hidden confounders.
method Double/debiased machine learning (DML) framework to reduce bias in two-stage IV regression.
result DML-IV outperforms state-of-the-art methods and learns high-performing policies.
Proposes a method to stabilize treatment effect estimation with unbalanced data.
problem Unbalanced treatment assignment leading to unstable propensity score estimations.
method Undersamples data for propensity score modeling and calibrates scores to match original distribution.
result The estimator retains asymptotic properties of the DML estimator and improves finite sample performance.
The paper provides guarantees for high-dimensional DML estimators in observational studies.
problem Estimating treatment effects in observational settings with many covariates.
method Debiased machine learning (DML) with finite-sample guarantees.
result Bounding the deviation of finite-sample distribution from asymptotic Gaussian approximation.
Double machine learning improves causal effect estimation by relaxing assumptions.
problem Estimating causal effects with observational data.
method Double/debiased machine learning (DML) framework.
result DML improves adjustment for nonlinear confounding relationships.
Double Machine Learning estimators are asymptotically inadmissible under structure-agnostic models.
problem Minimax estimators may be inadmissible under structure-agnostic models.
method Exhibit second-order (U-statistic) estimators that asymptotically dominate DML estimators.
result Double Machine Learning estimators are asymptotically inadmissible under structure-agnostic models.
Study improves statistical inference for CATEs using Lasso and DML.
problem Estimating and inferring CATEs in high-dimensional settings.
method Doubly robust estimator, Lasso regularization, debiased Lasso, DML.
result TDL (triple/debiased Lasso) achieves n \sqrt{n} n -consistency and confidence intervals. Prediction-powered causal inference achieves smaller asymptotic variance than traditional methods.
problem Estimating causal and structural parameters in a semi-supervised setting.
method Combining efficient influence function with debiased machine learning and semi-supervised Riesz regression.
result Asymptotic variances of estimators match the derived efficiency bound.
R package xtdml uses DML for panel data models with fixed effects.
problem Estimating structural parameters in panel data models with fixed effects.
method Combines machine learning with statistical estimation for inference.
result Demonstrates improved performance in learning nuisance functions.
Study assesses hyperparameter tuning for causal inference with DML.
problem Optimizing hyperparameters for causal inference with DML.
method Empirical simulation study using DML approach.
result Hyperparameter tuning crucial for causal estimation with DML.
Paper adapts DML for panel data, addressing unobserved heterogeneity.
problem Estimating causal effects with panel data and unobserved heterogeneity.
method Adapting double/debiased machine learning (DML) for panel data with predictive models based on correlated random effects.
result Predictive models based on correlated random effects within DML lead to accurate coefficient estimates.
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.
A scalable method for deep metric learning using chance constraints.
problem Improving deep metric learning by addressing feasibility issues.
method Relating DML to chance constraints, reformulating as a feasibility problem, and iteratively training proxies.
result The method effectively improves deep metric learning performance across multiple benchmarks.
Paper develops a new estimator for panel data with endogenous treatments, improving causal inference.
problem Challenges in causal inference for static panel data with endogenous treatments and confounding variables.
method Develops Double Machine Learning (DML) estimator for static panel models with endogenous treatments (panel IV DML). Introduces weak-identification diagnostics.
result Panel IV DML estimator improves estimation accuracy and delivers more reliable inference under weak identification.
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.
Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common DML approach is with triplet loss, despite significant advances in the area of DML. Triplet loss suffers from several issues such as collap…
Deep learning accelerates Heston model calibration.
problem Calibrating stochastic volatility models is computationally expensive.
method Differential Machine Learning (DML) technique to train neural networks on differentials of features and labels.
result DML reduces Heston model calibration time significantly.
Proposes AAA for efficient association estimation with confounders.
problem Summarizing log odds ratio as a function of confounders.
method Develops efficient DML estimators for AAA.
result Demonstrates practicality and effectiveness of AAA estimators.
This paper evaluates fairness in deep metric learning and proposes a method to reduce subgroup performance gaps.
problem The negative impact of deep metric learning representations on minority subgroup performance in downstream tasks.
method Definition of fairness in DML through inter-class, intra-class, and uniformity properties; finDML benchmark; Partial Attribute De-correlation (PARADE) method.
result Bias in DML representations propagates to downstream tasks, even with balanced training data.
The paper develops a method for self-normalized inference in adaptive experiments.
problem Adaptive experiments require a fixed horizon for ATE estimation, but propensities can change.
method The method uses self-normalized martingale limit theory to estimate ATE.
result The Studentized statistic is asymptotically N(0,1) at the prespecified horizon.
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.
A new probabilistic approach improves deep metric learning by considering image uncertainties and class-specific variances.
problem Proxy-based deep metric learning struggles with image uncertainties and class-specific structures.
method Introduces non-isotropic probabilistic proxy-based deep metric learning using directional von Mises-Fisher distributions.
result Improves generalization performance and competitive on standard benchmarks.
A new DML method for continuous treatments uncovers causal mediation effects.
problem Estimating causal mediation effects with continuous treatments.
method Double machine learning (DML) algorithm using kernel-based doubly robust moment function.
result Asymptotic normality with nonparametric convergence rate for estimating mediated response curve.
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.
Paper offers anytime-valid inference for causal parameters using DML.
problem Classic DML is only valid asymptotically for a fixed sample size.
method Time-uniform DML results for anytime-valid inference.
result Valid inference at any arbitrary stopping time.
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…
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.
With the remarkable success achieved by the Convolutional Neural Networks (CNNs) in object recognition recently, deep learning is being widely used in the computer vision community. Deep Metric Learning (DML), integrating deep learning with conventional metric learning, has set new records in many fields, especially in…
Develops DML for nonlinear panel data models with fixed effects.
problem Estimating causal effects in nonlinear panel data models with fixed effects.
method Double machine learning (DML) procedures for approximating nuisance functions.
result First-differencing yields the least constraints on fixed effects distribution.
This paper improves credit line impact analysis by considering spending as a distribution.
problem Previous studies on credit lines' impact on spending have overlooked the distributional nature of spending.
method Developed a distribution-valued estimator framework to extend existing real-valued estimators.
result Credit lines positively influence spending across all quantiles, but more towards luxuries as they increase.
The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in t…
Python package automates causal parameter estimation using Riesz regression.
problem Efficient estimation of causal and structural parameters.
method Automatic DML and generalized Riesz regression framework.
result Automatic construction of balancing link functions for generalized Riesz regression.
The study corrects measurement error in evaluating health effects of multiple pollutants.
problem Bias in estimating health effects of air pollution constituents due to mismeasurement.
method Used a linear regression calibration model and extended DML approach to correct for measurement error.
result Identified two PM2.5 constituents (Br and Mn) that show a negative causal effect on cognitive function after correction.
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.
Paper introduces new estimator for continuous treatment effects.
problem Estimating the average dose-response function of continuous treatments.
method Utilizes ADML and DML tools, with a novel debiasing method.
result Proves asymptotic normality and shows good performance in simulations.
New estimator optimizes black-box model errors in semiparametric estimation.
problem How nuisance estimation errors affect low-dimensional target parameters in semiparametric models.
method Proposed a new estimator achieving a sharper rate of convergence.
result The first-order stochastic error of nuisance estimation can be eliminated.
Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfac…
With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML…
Cross-entropy loss linked to metric learning, outperforming complex pairwise losses.
problem Improving metric learning performance without complex optimization schemes.
method Theoretical analysis linking cross-entropy to pairwise losses, showing cross-entropy as an upper bound and equivalent to mutual information maximization.
result Minimizing cross-entropy is equivalent to maximizing mutual information, leading to state-of-the-art performance.
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…
New method estimates extreme outcomes in heavy-tailed data, breaking circular dependence.
problem Estimating outcomes for extreme events in heavy-tailed data.
method Proposes an ADRF estimator that includes a structured tail-shape output and a diagnostic to evaluate tail shape.
result Successfully reduces MAE in deep-tail and conditional-shortfall predictions.
Study finds flipped classrooms improve student self-concept, enjoyment, but not exam scores.
problem Evaluating the impact of flipped classrooms on higher education outcomes.
method Double/debiased machine learning (DML) approach to analyze student data.
result No significant positive effects on exam scores, passing rates, or knowledge retention.
Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic classes. One major challenge in FGVC is the co-occurrence of two issues: 1) many subordinate classes are highly correlated and are difficult to distinguish, and 2) there exists the large intra-class variation (e…