# Targeted Synthetic Control Method

arXiv: https://arxiv.org/abs/2602.04611 · HTML: https://mlsanity.com/paper/2602.04611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 57 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 29 of 29)

- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations](https://mlsanity.com/paper/2002.04083/) (2020)
- [Orthogonal Statistical Learning](https://mlsanity.com/paper/1901.09036/) (2019)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Causal Transformer for Estimating Counterfactual Outcomes](https://mlsanity.com/paper/2204.07258/) (2022)
- [Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations](https://mlsanity.com/paper/2206.08311/) (2022)
- [Synthetic Difference in Differences](https://mlsanity.com/paper/1812.09970/) (2018)
- [An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls](https://mlsanity.com/paper/1712.09089/) (2017)
- [Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation](https://mlsanity.com/paper/2310.17463/) (2023)
- [The Augmented Synthetic Control Method](https://mlsanity.com/paper/1811.04170/) (2018)
- [Policy Analysis using Synthetic Controls in Continuous-Time](https://mlsanity.com/paper/2102.01577/) (2021)
- [Efficient Estimation of Pathwise Differentiable Target Parameters with the Undersmoothed Highly Adaptive Lasso](https://mlsanity.com/paper/1908.05607/) (2019)
- [On the Assumptions of Synthetic Control Methods](https://mlsanity.com/paper/2112.05671/) (2021)
- [On Weighted Orthogonal Learners for Heterogeneous Treatment Effects](https://mlsanity.com/paper/2303.12687/) (2023)
- [Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2505.13072/) (2025)
- [Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework](https://mlsanity.com/paper/2108.13935/) (2021)
- [Stabilized Neural Prediction of Potential Outcomes in Continuous Time](https://mlsanity.com/paper/2410.03514/) (2024)
- [Non-parametric identifiability and sensitivity analysis of synthetic control models](https://mlsanity.com/paper/2301.07656/) (2023)
- [Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control](https://mlsanity.com/paper/2305.00700/) (2023)
- [Proximal Causal Inference for Synthetic Control with Surrogates](https://mlsanity.com/paper/2308.09527/) (2023)
- [Spillover Detection for Donor Selection in Synthetic Control Models](https://mlsanity.com/paper/2406.11399/) (2024)
- [Differentially Private Synthetic Control](https://mlsanity.com/paper/2303.14084/) (2023)
- [Overlap-weighted orthogonal meta-learner for treatment effect estimation over time](https://mlsanity.com/paper/2510.19643/) (2025)
- [On the Misspecification of Linear Assumptions in Synthetic Control](https://mlsanity.com/paper/2302.12777/) (2023)
- [Cautionary Tales on Synthetic Controls in Survival Analyses](https://mlsanity.com/paper/2312.00501/) (2023)
- [A Meta-learner for Heterogeneous Effects in Difference-in-Differences](https://mlsanity.com/paper/2502.04699/) (2025)
- [DeepBlip: Estimating Conditional Average Treatment Effects Over Time](https://mlsanity.com/paper/2511.14545/) (2025)
- [Single Proxy Synthetic Control](https://mlsanity.com/paper/2307.16353/) (2023)
- [Caliper Synthetic Matching: Generalized Radius Matching with Local Synthetic Controls](https://mlsanity.com/paper/2411.05246/) (2024)

## Abstract (arXiv)

The synthetic control method (SCM) estimates causal effects in panel data with a single-treated unit by constructing a counterfactual outcome as a weighted combination of untreated control units that matches the pre-treatment trajectory. In this paper, we introduce the targeted synthetic control (TSC) method, a new two-stage estimator that directly estimates the counterfactual outcome. Specifically, our TSC method (1) yields a targeted debiasing estimator, in the sense that the targeted updating refines the initial weights to produce more stable weights; and (2) ensures that the final counterfactual estimation is a convex combination of observed control outcomes to enable direct interpretation of the synthetic control weights. TSC is flexible and can be instantiated with arbitrary machine learning models. Methodologically, TSC starts from an initial set of synthetic-control weights via a one-dimensional targeted update through the weight-tilting submodel, which calibrates the weights to reduce bias of weights estimation arising from pre-treatment fit. Furthermore, TSC avoids key shortcomings of existing methods (e.g., the augmented SCM), which can produce unbounded counterfactual estimates. Across extensive synthetic and real-world experiments, TSC consistently improves estimation accuracy over state-of-the-art SCM baselines.