# Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation

arXiv: https://arxiv.org/abs/2508.20335 · HTML: https://mlsanity.com/paper/2508.20335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Synthetic Difference in Differences](https://mlsanity.com/paper/1812.09970/) (2018)
- [Double Machine Learning for Static Panel Models with Fixed Effects](https://mlsanity.com/paper/2312.08174/) (2023)
- [Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning](https://mlsanity.com/paper/2407.19078/) (2024)

## Abstract (arXiv)

Accurately quantifying geo-level marketing lift in two-sided marketplaces is challenging: the Synthetic Control Method (SCM) often exhibits high power yet systematically under-estimates effect size, while panel-style Double Machine Learning (DML) is seldom benchmarked against SCM. We build an open, fully documented simulator that mimics a typical large-scale geo roll-out: N_unit regional markets are tracked for T_pre weeks before launch and for a further T_post-week campaign window, allowing all key parameters to be varied by the user and probe both families under five stylized stress tests: 1) curved baseline trends, 2) heterogeneous response lags, 3) treated-biased shocks, 4) a non-linear outcome link, and 5) a drifting control group trend. Seven estimators are evaluated: three standard Augmented SCM (ASC) variants and four panel-DML flavors (TWFE, CRE/Mundlak, first-difference, and within-group). Across 100 replications per scenario, ASC models consistently demonstrate severe bias and near-zero coverage in challenging scenarios involving nonlinearities or external shocks. By contrast, panel-DML variants dramatically reduce this bias and restore nominal 95%-CI coverage, proving far more robust. The results indicate that while ASC provides a simple baseline, it is unreliable in common, complex situations. We therefore propose a 'diagnose-first' framework where practitioners first identify the primary business challenge (e.g., nonlinear trends, response lags) and then select the specific DML model best suited for that scenario, providing a more robust and reliable blueprint for analyzing geo-experiments.