# Heterogeneous Synthetic Learner for Panel Data

arXiv: https://arxiv.org/abs/2212.14580 · HTML: https://mlsanity.com/paper/2212.14580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 30 Dec 2022.

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

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Off-policy evaluation for slate recommendation](https://mlsanity.com/paper/1605.04812/) (2016)
- [Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho](https://mlsanity.com/paper/2108.02836/) (2021)
- [Deep Neural Networks for Estimation and Inference](https://mlsanity.com/paper/1809.09953/) (2018)
- [Some methods for heterogeneous treatment effect estimation in high-dimensions](https://mlsanity.com/paper/1707.00102/) (2017)
- [An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls](https://mlsanity.com/paper/1712.09089/) (2017)
- [Synthetic Interventions](https://mlsanity.com/paper/2006.07691/) (2020)
- [The Augmented Synthetic Control Method](https://mlsanity.com/paper/1811.04170/) (2018)
- [On the Assumptions of Synthetic Control Methods](https://mlsanity.com/paper/2112.05671/) (2021)
- [Synthetic learner: model-free inference on treatments over time](https://mlsanity.com/paper/1904.01490/) (2019)
- [A Design-Based Perspective on Synthetic Control Methods](https://mlsanity.com/paper/2101.09398/) (2021)
- [Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs](https://mlsanity.com/paper/1905.11622/) (2019)
- [Synthetic Control, Synthetic Interventions, and COVID-19 spread: Exploring the impact of lockdown measures and herd immunity](https://mlsanity.com/paper/2009.09987/) (2020)
- [Detecting Heterogeneous Treatment Effect with Instrumental Variables](https://mlsanity.com/paper/1908.03652/) (2019)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data](https://mlsanity.com/paper/2505.09496/) (2025)
- [Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data](https://mlsanity.com/paper/2510.22828/) (2025)

## Abstract (arXiv)

In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and cannot handle the non-stationarity and temporal dependency in the common panel data setting. The treatment evaluators developed for panel data, on the other hand, typically ignore the individualized information. To fill the gap, in this paper, we initialize the study of HTE estimation in panel data. Under different assumptions for HTE identifiability, we propose the corresponding heterogeneous one-side and two-side synthetic learner, namely H1SL and H2SL, by leveraging the state-of-the-art HTE estimator for non-panel data and generalizing the synthetic control method that allows flexible data generating process. We establish the convergence rates of the proposed estimators. The superior performance of the proposed methods over existing ones is demonstrated by extensive numerical studies.