# Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift

arXiv: https://arxiv.org/abs/2504.02694 · HTML: https://mlsanity.com/paper/2504.02694/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 18 of 18)

- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Semiparametric counterfactual density estimation](https://mlsanity.com/paper/2102.12034/) (2021)
- [Nonparametric causal effects based on incremental propensity score interventions](https://mlsanity.com/paper/1704.00211/) (2017)
- [Counterfactual Predictions under Runtime Confounding](https://mlsanity.com/paper/2006.16916/) (2020)
- [Algorithms for Fitting the Constrained Lasso](https://mlsanity.com/paper/1611.01511/) (2016)
- [FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes](https://mlsanity.com/paper/2109.00173/) (2021)
- [Prediction meets causal inference: the role of treatment in clinical prediction models](https://mlsanity.com/paper/2004.06998/) (2020)
- [Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning](https://mlsanity.com/paper/2306.03625/) (2023)
- [Doubly Robust Counterfactual Classification](https://mlsanity.com/paper/2301.06199/) (2023)
- [Generalized Linear Models with Linear Constraints for Microbiome Compositional Data](https://mlsanity.com/paper/1801.03238/) (2018)
- [Incremental effects for continuous exposures](https://mlsanity.com/paper/2409.11967/) (2024)
- [Counterfactual Mean-variance Optimization](https://mlsanity.com/paper/2209.09538/) (2022)
- [Nonparametric Estimation of Conditional Incremental Effects](https://mlsanity.com/paper/2212.03578/) (2022)
- [Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models](https://mlsanity.com/paper/1709.06859/) (2017)
- [Holistic Generalized Linear Models](https://mlsanity.com/paper/2205.15447/) (2022)
- [Treatment Choice, Mean Square Regret and Partial Identification](https://mlsanity.com/paper/2310.06242/) (2023)
- [Incremental Intervention Effects in Studies with Dropout and Many Timepoints](https://mlsanity.com/paper/1907.04004/) (2019)

## Built on by (most cited first, 1 of 1)

- [Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach](https://mlsanity.com/paper/2603.27320/) (2026)

## 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.