# Transfer Learning for Causal Effect Estimation

arXiv: https://arxiv.org/abs/2305.09126 · HTML: https://mlsanity.com/paper/2305.09126/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 74 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 11 of 11)

- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms](https://mlsanity.com/paper/2101.10943/) (2021)
- [Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2202.12891/) (2022)
- [In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2302.02923/) (2023)
- [Domain adaptation under structural causal models](https://mlsanity.com/paper/2010.15764/) (2020)
- [Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation](https://mlsanity.com/paper/2210.06183/) (2022)
- [Transfer Learning for Estimating Causal Effects using Neural Networks](https://mlsanity.com/paper/1808.07804/) (2018)
- [The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation](https://mlsanity.com/paper/2303.01412/) (2023)
- [Generalized generalized linear models: Convex estimation and online bounds](https://mlsanity.com/paper/2304.13793/) (2023)
- [Estimating individual treatment effect: generalization bounds and algorithms](https://mlsanity.com/paper/1606.03976/) (2016)
- [Source-Function Weighted-Transfer Learning for Nonparametric Regression with Seemingly Similar Sources](https://mlsanity.com/paper/2302.11222/) (2023)

## Built on by (most cited first, 3 of 3)

- [Causal Transfer in Medical Image Analysis](https://mlsanity.com/paper/2603.24388/) (2026)
- [Transfer learning for causal forest](https://mlsanity.com/paper/2606.07693/) (2026)
- [Transportable Causal Effect Estimation across Networks under Interference](https://mlsanity.com/paper/2608.18932/) (2026)

## Abstract (arXiv)

We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data. Limited data is very common in medical applications, where some rare medical conditions, such as sepsis, are of interest. Our proposed method, named textttℓ1-TCL, incorporates ℓ1 regularized TL for nuisance models (e.g., propensity score model); the TL estimator of the nuisance parameters is plugged into downstream average causal/treatment effect estimators (e.g., inverse probability weighted estimator). We establish non-asymptotic recovery guarantees for the textttℓ1-TCL with generalized linear model (GLM) under the sparsity assumption in the high-dimensional setting, and demonstrate the empirical benefits of textttℓ1-TCL through extensive numerical simulation for GLM and recent neural network nuisance models. Our method is subsequently extended to real data and generates meaningful insights consistent with medical literature, a case where all baseline methods fail.