mlsanityData to 7 Oct 2026

Non-parametric identifiability and sensitivity analysis of synthetic control models

arXiv 2301.07656 ↗First posted 18 Jan 2023stat.ME cs.AI cs.LGcited by 5

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Abstract (arXiv)

Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situations such trials cannot be performed. In the absence of such trials, many methods have been devised to quantify the causal impact of an intervention from observational data given certain assumptions. One widely used method are synthetic control models. While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention. This is a strong assumption, as synthetic control models can only be learned in pre-intervention period. In this paper we address this challenge, and prove identifiability can be obtained without the need for this assumption, by showing it follows from the principle of invariant causal mechanisms. Moreover, for the first time, we formulate and study synthetic control models in Pearl's structural causal model framework. Importantly, we provide a general framework for sensitivity analysis of synthetic control causal inference to violations of the assumptions underlying non-parametric identifiability. We end by providing an empirical demonstration of our sensitivity analysis framework on simulated and real data in the widely-used linear synthetic control framework.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 18 Jan 2023.

Builds on

18 of 36 references.

References · 18

  1. 2020Representation Learning via Invariant Causal Mechanismscited by 197
  2. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
  3. 2017Learning Independent Causal Mechanismscited by 104
  4. 2020An Introduction to Proximal Causal Learningcited by 91
  5. 2022Out-of-distribution Generalization with Causal Invariant Transformationscited by 45
  6. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  7. 2022Invariant Causal Mechanisms through Distribution Matchingcited by 31
  8. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  9. 2022D'ARTAGNAN: Counterfactual Video Generationcited by 27
  10. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
  11. 2019Integrating overlapping datasets using bivariate causal discoverycited by 12
  12. 2021On the Assumptions of Synthetic Control Methodscited by 12
6 more
  1. 2021The Proximal ID Algorithmcited by 11
  2. 2022Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounderscited by 10
  3. 2019Leveraging directed causal discovery to detect latent common causescited by 9
  4. 2021Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Frameworkcited by 9
  5. 2015Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variablescited by 6
  6. 2019MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programmingcited by 4

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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2024Contrastive representations of high-dimensional, structured treatmentscited by 7
  2. 2024Spillover Detection for Donor Selection in Synthetic Control Modelscited by 4
  3. 2025Local Interference: Removing Interference Bias in Semi-Parametric Causal Modelscited by 1
  4. 2025Quantum oracles give an advantage for identifying classical counterfactualscited by 0
  5. 2026Targeted Synthetic Control Methodcited by 0