Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift
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Abstract (arXiv)
We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data. This problem is central to decision-making under distribution shift, where treatment patterns may change at deployment. We develop a semiparametric framework for counterfactual regression along a prespecified incremental-intervention path. The target is a finite-dimensional constrained projection of counterfactual risk, estimated using cross-fitted influence-function representations of the program components. For smooth programs with fixed constraints and finite-dimensional programs with estimated linear constraints, we establish consistency and local stability of the optimizer under class-specific conditions, and derive pointwise and uniform first-order expansions. These results yield asymptotically valid inference, including simultaneous confidence bands for the counterfactual regression path. Simulations and an application to SMS reminders illustrate the finite-sample performance and practical applicability of the proposed approach.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 51 references that are in the map. First posted on arXiv 3 Apr 2025.
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18 of 51 references.
References · 18
- 2016Learning Representations for Counterfactual Inferencecited by 329
- 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 133
- 2021Semiparametric counterfactual density estimationcited by 42
- 2017Nonparametric causal effects based on incremental propensity score interventionscited by 28
- 2020Counterfactual Predictions under Runtime Confoundingcited by 22
- 2016Algorithms for Fitting the Constrained Lassocited by 18
- 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
- 2020Prediction meets causal inference: the role of treatment in clinical prediction modelscited by 7
- 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learningcited by 6
- 2023Doubly Robust Counterfactual Classificationcited by 5
- 2018Generalized Linear Models with Linear Constraints for Microbiome Compositional Datacited by 4
- 2024Incremental effects for continuous exposurescited by 4
6 more
- 2022Counterfactual Mean-variance Optimizationcited by 3
- 2022Nonparametric Estimation of Conditional Incremental Effectscited by 3
- 2017Using marginal structural models to adjust for treatment drop-in when developing clinical prediction modelscited by 2
- 2022Holistic Generalized Linear Modelscited by 2
- 2023Treatment Choice, Mean Square Regret and Partial Identificationcited by 2
- 2019Incremental Intervention Effects in Studies with Dropout and Many Timepointscited by 1
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Citing papers per year: 2026: 1.
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