# The Augmented Synthetic Control Method

arXiv: https://arxiv.org/abs/1811.04170 · HTML: https://mlsanity.com/paper/1811.04170/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2018.

## Built on by (most cited first, 25 of 25)

- [Augmented balancing weights as linear regression](https://mlsanity.com/paper/2304.14545/) (2023)
- [Synthetically Controlled Bandits](https://mlsanity.com/paper/2202.07079/) (2022)
- [Learning Treatment Effects in Panels with General Intervention Patterns](https://mlsanity.com/paper/2106.02780/) (2021)
- [Synthetic Control As Online Linear Regression](https://mlsanity.com/paper/2202.08426/) (2022)
- [Adaptive Principal Component Regression with Applications to Panel Data](https://mlsanity.com/paper/2307.01357/) (2023)
- [Strategyproof Decision-Making in Panel Data Settings and Beyond](https://mlsanity.com/paper/2211.14236/) (2022)
- [Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control](https://mlsanity.com/paper/2305.00700/) (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)
- [Heterogeneous Synthetic Learner for Panel Data](https://mlsanity.com/paper/2212.14580/) (2022)
- [Asymptotically Unbiased Synthetic Control Methods by Moment Matching](https://mlsanity.com/paper/2307.11127/) (2023)
- [SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation](https://mlsanity.com/paper/2604.07513/) (2026)
- [ClusterSC: Advancing Synthetic Control with Donor Selection](https://mlsanity.com/paper/2503.21629/) (2025)
- [Regularizing Extrapolation in Causal Inference](https://mlsanity.com/paper/2509.17180/) (2025)
- [Time-Aware Synthetic Control](https://mlsanity.com/paper/2601.03099/) (2026)
- [Dynamic covariate balancing: estimating treatment effects over time with potential local projections](https://mlsanity.com/paper/2103.01280/) (2021)
- [Predictor Selection for Synthetic Controls](https://mlsanity.com/paper/2203.11576/) (2022)
- [Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle](https://mlsanity.com/paper/2301.12571/) (2023)
- [Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data](https://mlsanity.com/paper/2510.22828/) (2025)
- [Counterfactual Forecasting for Panel Data](https://mlsanity.com/paper/2511.06189/) (2025)
- [Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach](https://mlsanity.com/paper/2512.24747/) (2025)
- [Targeted Synthetic Control Method](https://mlsanity.com/paper/2602.04611/) (2026)
- [Spectral Truncation in Synthetic Control](https://mlsanity.com/paper/2607.25074/) (2026)
- [Conformalized Rate-Adaptive Sensing](https://mlsanity.com/paper/2607.26887/) (2026)
- [Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data](https://mlsanity.com/paper/2609.13586/) (2026)

## Abstract (arXiv)

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pre-treatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pre-treatment fit is infeasible. Analogous to bias correction for inexact matching, Augmented SCM uses an outcome model to estimate the bias due to imperfect pre-treatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pre-treatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package.