Targeted Synthetic Control Method

TL;DR

The targeted synthetic control (TSC) method improves causal effect estimation in panel data by providing a debiased, interpretable, and flexible estimator that outperforms existing SCM approaches in accuracy.

Contribution

The paper introduces TSC, a novel two-stage estimator that enhances synthetic control methods with targeted debiasing and interpretability, applicable with machine learning models.

Findings

TSC reduces bias in weight estimation.

TSC produces more stable and interpretable counterfactuals.

TSC outperforms existing SCM methods in synthetic and real data.

Abstract

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.