Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Synthetic Interventions: Extending Synthetic Controls to Multiple Treatments. |
| Συγγραφείς: |
Agarwal, Anish1 (AUTHOR) aa5194@columbia.edu, Shah, Devavrat2 (AUTHOR) devavrat@mit.edu, Shen, Dennis3 (AUTHOR) dennis.shen@marshall.usc.edu |
| Πηγή: |
Operations Research. Mar/Apr2026, Vol. 74 Issue 2, p840-859. 20p. |
| Θεματικοί όροι: |
Causal inference, Counterfactuals (Logic), Policy analysis, Inferential statistics |
| Περίληψη: |
Traditional methods for policy evaluation typically focus on a single intervention. Yet many real-world settings feature multiple, often concurrent, interventions—making it crucial to understand their comparative effects. In "Synthetic Interventions: Extending Synthetic Controls to Multiple Treatments," Agarwal, Shah, and Shen introduce the synthetic interventions framework, a generalization of the synthetic controls method that accommodates multiple treatments within a unified model. By representing outcomes as a low-rank tensor capturing relationships across time, units, and interventions, their approach enables researchers to estimate counterfactual outcomes under all interventions for each unit. The authors establish consistency of their estimator and show that a bias-corrected version achieves asymptotic normality, permitting valid statistical inference. This work offers a new perspective for evaluating complex, multipolicy environments where traditional causal inference tools fall short. The synthetic controls (SC) methodology is a prominent tool for policy evaluation in panel data applications, typically grounded in a low-rank matrix factor model where potential outcomes are governed by low-dimensional latent factors over units and time. In this article, we present the synthetic interventions (SI) framework, an extension of the SC framework to accommodate multiple interventions. Fundamental to SI is a low-rank tensor factor model that builds on the matrix structure by embedding a latent factorization over interventions within the temporal factors, and assuming that unit-level latent factors are shared across both time and interventions. To operationalize this framework, we generalize standard SC-based estimators by borrowing strength across interventions, all while preserving their core principles. We establish consistency for one estimator variant and show that a bias-corrected version achieves asymptotic normality, thereby enabling valid inference. Our statistical results are supported by simulation studies and an empirical revisit of a canonical SC tobacco control case study, where we explore related questions not previously examined. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2025.1590. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
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