Papers › Why Synthetic Control estimators are biased and what to do about it: Introducing...
Why Synthetic Control estimators are biased and what to do about it: Introducing Relaxed and Penalized Synthetic Controls
Oscar Engelbrektson
This paper extends the literature on the theoretical properties of synthetic controls to the case of non-linear generative models, showing that the synthetic control estimator is generally biased in such settings. I derive a lower bound for the bias, showing that the only component of it that is affected by the choice of synthetic control is the weighted sum of pairwise differences between the treated unit and the untreated units in the synthetic control. To address this bias, I propose a novel synthetic control estimator that allows for a constant difference of the synthetic control to the treated unit in the pre-treatment period, and that penalizes the pairwise discrepancies. Allowing for a constant offset makes the model more flexible, thus creating a larger set of potential synthetic controls, and the penalization term allows for the selection of the potential solution that will minimize bias. I study the properties of this estimator and propose a data-driven process for parameterizing the penalization term.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections