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.
Book introduces ML and AI for causal inference.
problem Uncertainty in causal relationships.
method Structural equation models, DAGs, SCMs, and Double/Debiased Machine Learning.
result Improved inference in causal models using predictive tools.
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.
ddml aids causal inference in econometrics with machine learning.
problem Estimation of causal effects with endogenous variables and unknown functional forms.
method Double/Debiased Machine Learning (DDML) in Stata.
result Monte Carlo evidence supports using DDML with stacking for causal inference.
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.
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.
New method improves robustness of neural network-based debiasing.
problem Improving robustness of neural network-based debiasing.
method Moment-constrained learning for neural networks.
result Improved performance compared to state-of-the-art benchmarks.
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.
Estimates impulse response functions using machine learning in time series data.
problem Estimating causal effects of discrete treatments over time with flexible models.
method Double/debiased machine learning for nonparametric time series data.
result Consistent and asymptotically normal estimator for impulse response functions.
Proposes efficient estimators for weighted cumulative treatment effects in observational studies.
problem Inconsistent and inefficient estimators due to model misspecification and lack of overlap.
method Double/debiased machine learning for weighted cumulative causal effects.
result Proposed estimators are consistent, asymptotically linear, and reach semiparametric efficiency bounds.
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.
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.
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.
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. Method estimates dynamic treatment effects using machine learning and g-estimation.
problem Estimating treatment effects over time with multiple treatments and potential future outcomes.
method Double/debiased machine learning framework for dynamic treatment effects, extending Neyman orthogonal cross-fitted g g g -estimation. result Provides finite sample guarantees and allows for non-linear effect heterogeneity and high-dimensional parameterizations.
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.
The paper uses machine learning to optimize rework policies in semiconductor manufacturing.
problem Optimizing rework steps to increase yield without increasing costs.
method Applied double/debiased machine learning (DML) to estimate treatment effects.
result Derived optimal rework policies and estimated their value empirically.
Unified framework for automatic debiased machine learning for various statistical parameters.
problem Inference on smooth functionals of nonparametric M-estimands.
method Unified framework using gradient, Hessian, and linear approximation; solves two risk minimization problems.
result Efficient autoDML estimators with double robustness and robustness to misspecification.
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.
Estimates the effect of time-varying treatments using machine learning.
problem Estimating the impact of time-varying treatments over multiple periods.
method Difference-in-Differences framework with double/debiased machine learning.
result Higher vaccination rates reduce COVID-19 mortality after several weeks.
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…
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.
The paper uses causal machine learning to optimize rework decisions in manufacturing.
problem Optimizing rework policies in manufacturing systems to balance yield improvement and rework costs.
method Proposes a causal model using double/debiased machine learning (DML) techniques to estimate conditional treatment effects and derive rework policies.
result Achieved a yield improvement of 2-3% during the color-conversion process of white LEDs.
Estimates causal effects using machine learning for binary treatment and mediator.
problem Estimating direct and indirect quantile treatment effects under selection-on-observables.
method Double/debiased machine learning estimators based on efficient score functions.
result Uniform consistency and asymptotic normality of effect estimators.
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.
Bayesian method corrects bias in treatment effect estimation.
problem Estimating treatment effects from observational data with high-dimensional nuisance parameters.
method Bayesian debiasing, targeted modeling, sample splitting.
result Marginal posterior for ATE satisfies Bernstein-von Mises theorem under correct nuisance model specification.
C-Learner improves stability of plug-in estimators for causal inference.
problem Limited overlap between treatment and control groups leads to unstable estimates.
method Constrained learning framework that achieves stability and asymptotic properties.
result Constrained learning produces stable estimates with desirable asymptotic properties.
Although various distributed machine learning schemes have been proposed recently for pure linear models and fully nonparametric models, little attention has been paid on distributed optimization for semi-paramemetric models with multiple-level structures (e.g. sparsity, linearity and nonlinearity). To address these is…
Generative models are reinterpreted in statistical terms, enabling better understanding and inference.
problem Insufficient interpretability of generative models in statistical terms.
method Flow matching and orthogonalization/cross-fitting in double/debiased machine learning.
result Generative models can be used to estimate nuisance components while maintaining inferential validity.
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.
Unified framework for causal inference under sample selection.
problem Causal inference under sample selection with treatment and outcome non-randomness.
method ForestRiesz estimator, Riesz representation framework.
result ForestRiesz estimator yields more stable treatment effect estimates than conventional double machine learning approaches.
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.
Optimal first-order methods are shown to be fundamental limits in functional estimation.
problem Optimal functional estimation under weak conditions.
method Formalization of functional estimation with black-box nuisance function estimates and derivation of minimax lower bounds.
result First-order methods are optimal under weak conditions, but higher-order methods can outperform them when nuisance function structure is known.
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.
D3M debiases models by selectively removing problematic examples.
problem Model failures on underrepresented subgroups.
method Isolates and removes specific training examples that cause failures.
result Efficiently trains debiased classifiers with minimal example removal.
New method measures treatment effects across different groups.
problem Understanding treatment effects across subgroups while accounting for covariates.
method Proposes BGATE, a new parameter for balanced group average treatment effect.
result Demonstrates usefulness of BGATE in estimating treatment heterogeneity.
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.
A self-supervised debiasing method using rank regularization mitigates spurious correlations in neural networks.
problem Spurious correlations cause biases in deep neural networks, affecting generalization.
method Spectral analysis of latent representations, rank regularization, self-supervised pretraining, debiasing of downstream tasks.
result The proposed framework significantly improves generalization performance and outperforms supervised debiasing approaches.
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 three-stage debiasing framework to improve out-of-distribution accuracy.
problem Inaccurate uncertainty estimations in bias-only models damage ensemble-based debiasing performance.
method Calibrates the bias-only model to improve its uncertainty estimations, creating a three-stage ensemble-based debiasing framework.
result The three-stage debiasing framework consistently outperforms traditional methods in out-of-distribution accuracy.
Proposes a method to estimate causal effects of continuous treatments using instrumental variables.
problem Estimating causal effects of continuous treatments in the presence of unmeasured confounders.
method Introduces a novel framework using instrumental variables and a uniform regular weighting function to identify and estimate average dose-response functions.
result Establishes the asymptotic properties of the proposed methods for estimating average dose-response functions.
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.
A debiasing method improves nonparametric regression's statistical properties.
problem Lack of theoretical guarantees for modern nonparametric regression methods.
method Model-free debiasing method incorporating a correction term.
result Debiased estimator satisfies pointwise and uniform risk convergence, asymptotic normality.
COMMOD debiases models with minimal and interpretable changes.
problem Inconsistent and costly model updates in fair machine learning.
method Introduced COMMOD, a novel algorithm for algorithmic fairness that minimizes changes and makes them interpretable.
result COMMOD achieves comparable performance to state-of-the-art debiasing methods while making minimal and interpretable changes.
Mitigates confirmation bias in SSL by adjusting pseudo labels dynamically.
problem Confirmation bias in semi-supervised learning leads to errors in pseudo labels.
method TaMatch framework adjusts scaling ratio to debias pseudo labels and dynamically adjusts target distribution.
result TaMatch significantly outperforms existing methods in SSL tasks.
We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model -- an actively studied topic in statistics and machine learning. In the noi…
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.
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.