mlsanityData to 7 Oct 2026

Spillover Detection for Donor Selection in Synthetic Control Models

arXiv 2406.11399 ↗First posted 17 Jun 2024stat.ME stat.MLcited by 4

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Abstract (arXiv)

Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of correlated units that are not impacted by the intervention. Given one of these potential donor units, how can we decide whether it is in fact a valid donor - that is, one not subject to spillover effects from the intervention? Such a decision typically requires appealing to strong a priori domain knowledge specifying the units, which becomes infeasible in situations with large pools of potential donors. In this paper, we introduce a practical, theoretically-grounded donor selection procedure, aiming to weaken this domain knowledge requirement. Our main result is a Theorem that yields the assumptions required to identify donor values at post-intervention time points using only pre-intervention data. We show how this Theorem - and the assumptions underpinning it - can be turned into a practical method for detecting potential spillover effects and excluding invalid donors when constructing SCs. Importantly, we employ sensitivity analysis to formally bound the bias in our SC causal estimate in situations where an excluded donor was indeed valid, or where a selected donor was invalid. Using ideas from the proximal causal inference and instrumental variables literature, we show that the excluded donors can nevertheless be leveraged to further debias causal effect estimates. Finally, we illustrate our donor selection procedure on both simulated and real-world datasets.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 44 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

22 of 44 references.

References · 22

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  2. 2020An Introduction to Proximal Causal Learningcited by 91
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  4. 2017An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controlscited by 36
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  7. 2022D'ARTAGNAN: Counterfactual Video Generationcited by 27
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  10. 2020A Selective Review of Negative Control Methods in Epidemiologycited by 15
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10 more
  1. 2021The Proximal ID Algorithmcited by 11
  2. 2023Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confoundingcited by 11
  3. 2022Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounderscited by 10
  4. 2019Leveraging directed causal discovery to detect latent common causescited by 9
  5. 2021Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Frameworkcited by 9
  6. 2015Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variablescited by 6
  7. 2023Non-parametric identifiability and sensitivity analysis of synthetic control modelscited by 5
  8. 2019MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programmingcited by 4
  9. 2023Proximal Causal Inference for Synthetic Control with Surrogatescited by 4
  10. 2023Balancing Approach for Causal Inference at Scalecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Contrastive representations of high-dimensional, structured treatmentscited by 7
  2. 2025Local Interference: Removing Interference Bias in Semi-Parametric Causal Modelscited by 1
  3. 2025Quantum oracles give an advantage for identifying classical counterfactualscited by 0
  4. 2026Targeted Synthetic Control Methodcited by 0