# Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes

arXiv: https://arxiv.org/abs/2502.07295 · HTML: https://mlsanity.com/paper/2502.07295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Learning Counterfactual Representations for Estimating Individual Dose-Response Curves](https://mlsanity.com/paper/1902.00981/) (2019)
- [Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks](https://mlsanity.com/paper/2002.12326/) (2020)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [Semiparametric counterfactual density estimation](https://mlsanity.com/paper/2102.12034/) (2021)
- [Learning Weighted Representations for Generalization Across Designs](https://mlsanity.com/paper/1802.08598/) (2018)
- [Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts](https://mlsanity.com/paper/2402.01972/) (2024)
- [Adversarially Balanced Representation for Continuous Treatment Effect Estimation](https://mlsanity.com/paper/2312.10570/) (2023)
- [Estimating Heterogeneous Treatment Effects for General Responses](https://mlsanity.com/paper/2103.04277/) (2021)
- [Orthogonal prediction of counterfactual outcomes](https://mlsanity.com/paper/2311.09423/) (2023)

## Abstract (arXiv)

Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properties -- doubly robustness and fast convergence rates -- remains challenging. A common approach to address this is targeted regularization, which modifies the objective function of NNs. However, existing work on neural causal effect estimation is largely limited to continuous outcomes, restricting its applicability to settings involving binary, count, or other skewed outcomes commonly encountered in practice. We propose a unified targeted regularization framework for the Exponential Dispersion Family (EDF) to address this limitation. Specifically, we first derive the von Mises expansion of the average dose function of canonical functions (ADCF) for discrete treatments and of the sieve-projected ADCF for continuous treatments. Second, we use this expansion to construct a unified targeted regularization, that corrects first-order bias at the distributional level. We integrate this objective into a NN architecture that jointly estimates the outcome model, propensity score model, and fluctuation parameter end-to-end. Experimental results demonstrate the effectiveness of our method.