Data to 14 Oct 2026

Double/Debiased/Neyman Machine Learning of Treatment Effects

arXiv 1701.08687 ↗First posted 30 Jan 2017stat.ML stat.MEcited by 76

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Abstract (arXiv)

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 generation of nonparametric fitting methods for high-dimensional data, called machine learning methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects (ATE) and average treatment effects on the treated (ATTE) using observational data. A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).

Key facts. Cited by 76 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jan 2017.

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Citing papers per year: 2021: 11; 2022: 18; 2023: 19; 2024: 12; 2025: 9; 2026: 7.

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2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
2021Outside the Echo Chamber: Optimizing the Performative Riskcited by 96
2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 61
2021Deeply-Debiased Off-Policy Interval Estimationcited by 36
2021Federated Causal Inference in Heterogeneous Observational Datacited by 24
2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
2024DiffPO: A causal diffusion model for learning distributions of potential outcomescited by 17
2022Causal Estimation for Text Data with (Apparent) Overlap Violationscited by 15
2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
2023One-Step Estimation of Differentiable Hilbert-Valued Parameterscited by 11
2023Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learningcited by 11

38 more

2023Counterfactual Prediction Under Outcome Measurement Errorcited by 10
2024Simplifying debiased inference via automatic differentiation and probabilistic programmingcited by 10
2022Generalization bounds and algorithms for estimating conditional average treatment effect of dosagecited by 9
2023Source Condition Double Robust Inference on Functionals of Inverse Problemscited by 9
2022Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictionscited by 8
2022Neighborhood Adaptive Estimators for Causal Inference under Network Interferencecited by 8
2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learningcited by 6
2024Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimationcited by 6
2025Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairnesscited by 6
2021Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfallscited by 5
2022Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimationcited by 5
2023Doubly Robust Counterfactual Classificationcited by 5
2023Estimation Beyond Data Reweighting: Kernel Method of Momentscited by 5
2023Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameterscited by 5
2023Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shiftcited by 5
2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 5
2021Causal Gradient Boosting: Boosted Instrumental Variable Regressioncited by 4
2022A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statisticscited by 4
2022Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approachcited by 4
2022Double Robust Bayesian Inference on Average Treatment Effectscited by 4
2025Adjustment for Confounding using Pre-Trained Representationscited by 4
2021Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspectscited by 3
2023A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testingcited by 3
2023Model-based causal feature selection for general response typescited by 3
2024Uplift Modeling Under Limited Supervisioncited by 3
2024Causal K-Means Clusteringcited by 3
2024Off-policy estimation with adaptively collected data: the power of online learningcited by 3
2026Optimally taming biases in black-box models for efficient semiparametric estimationcited by 3
2022Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activitiescited by 2
2022Adversarial Estimatorscited by 2
2022A Causal Research Pipeline and Tutorial for Psychologists and Social Scientistscited by 2
2023Causal Forecasting for Pricingcited by 2
2024Causal inference through multi-stage learning and doubly robust deep neural networkscited by 2
2024Semiparametric inference for impulse response functions using double/debiased machine learningcited by 2
2025A Meta-learner for Heterogeneous Effects in Difference-in-Differencescited by 2
2025A Two-armed Bandit Framework for A/B Testingcited by 2
2021Optimal Targeting in Fundraising: A Causal Machine-Learning Approachcited by 1
2021A framework for causal segmentation analysis with machine learning in large-scale digital experimentscited by 1

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