mlsanityData to 7 Oct 2026

Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations

arXiv 1309.4686 ↗First posted 18 Sep 2013math.ST econ.EM stat.MEcited by 38

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Abstract (arXiv)

This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a doubly-robust estimator that are robust to model selection errors and prove that they are valid uniformly over a large class of treatment effect models. The class allows for multivalued treatments with heterogeneous effects (in observables), general heteroskedasticity, and selection amongst (possibly) more covariates than observations. Our estimator attains the semiparametric efficiency bound under appropriate conditions. Precise conditions are given for any model selector to yield these results, and we show how to combine data-driven selection with economic theory. For implementation, we give a specific proposal for selection based on the group lasso, which is particularly well-suited to treatment effects data, and derive new results for high-dimensional, sparse multinomial logistic regression. A simulation study shows our estimator performs very well in finite samples over a wide range of models. Revisiting the National Supported Work demonstration data, our method yields accurate estimates and tight confidence intervals.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2013.

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Citing papers per year: 2020: 1; 2021: 7; 2022: 8; 2023: 7; 2024: 6; 2025: 5; 2026: 4.

38 papers.

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  1. 2023Augmented balancing weights as linear regressioncited by 21
  2. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  3. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  4. 2021β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlapcited by 8
  5. 2022Neural Score Matching for High-Dimensional Causal Inferencecited by 6
  6. 2022A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyondcited by 5
  7. 2024Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimationcited by 5
  8. 2021Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlapcited by 4
  9. 2021Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learningcited by 4
  10. 2022Double Robust Bayesian Inference on Average Treatment Effectscited by 4
  11. 2023Variable Importance Matching for Causal Inferencecited by 4
  12. 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Datacited by 3
26 more
  1. 2026Optimally taming biases in black-box models for efficient semiparametric estimationcited by 3
  2. 2022Robust Causal Learning for the Estimation of Average Treatment Effectscited by 2
  3. 2023Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimatorscited by 2
  4. 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimatorscited by 2
  5. 2024Calibrating doubly-robust estimators with unbalanced treatment assignmentcited by 2
  6. 2024Causal inference through multi-stage learning and doubly robust deep neural networkscited by 2
  7. 2025Factor Informed Double Deep Learning For Average Treatment Effect Estimationcited by 2
  8. 2020Causal Inference in Possibly Nonlinear Factor Modelscited by 1
  9. 2021Normalized Augmented Inverse Probability Weighting with Neural Network Predictionscited by 1
  10. 2022Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Informationcited by 1
  11. 2023Convolutional neural networks for valid and efficient causal inferencecited by 1
  12. 2023Active and Passive Causal Inference Learningcited by 1
  13. 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
  14. 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
  15. 2021Generalized Linear Models with Structured Sparsity Estimatorscited by 0
  16. 2021An Interpretable Neural Network for Parameter Inferencecited by 0
  17. 2022Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarizationcited by 0
  18. 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
  19. 2023Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variablescited by 0
  20. 2024Valid causal inference with unobserved confounding in high-dimensional settingscited by 0
  21. 2025Rate doubly robust estimation for weighted average treatment effectscited by 0
  22. 2025Incorporating External Controls for Estimating the Average Treatment Effect on the Treated with High-Dimensional Data: Retaining Double Robustness and Ensuring Double Safetycited by 0
  23. 2025The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inferencecited by 0
  24. 2026Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonalitycited by 0
  25. 2026Shrinkage priors for Bayesian Substitute Confounderscited by 0
  26. 2026Path-specific harm decomposition: A partial identification frameworkcited by 0