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IR20-005_Albalate+Bel+Mazaira.pdf (1.18 MB)

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Working paper

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2020

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cc-by-nc-nd, (c) Albalate et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/158714

Ensuring Stability, Accuracy and Meaningfulness in Synthetic Control Methods: The Regularized SHAP-Distance Method

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Abstract

The synthetic control method (SCM) has been increasingly adopted to evaluate causal effects under quasi-experimental designs. However, SCM suffers from sound weaknesses that compromise its accuracy, stability and meaningfulness. The Regularized SHAP-distance synthetic control method (RSD-SCM) is proposed as solution. We evaluate the economic effect of the government formation deadlock in Spain, 2016. The deadlock did not negatively affect economic growth, as the economy grew 1.58% more without full government; standard SCM method overestimates the effect by 0.23 pp. We show that RSD-SCM offers higher accuracy and stability, while ensuring the economic meaningfulness of covariates used in building the counterfactual.

Subject

Models economètrics, Control de gestió, Causalitat

Subject (English)

Econometric models, Management audit, Causation

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Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

Citation

ALBALATE, Daniel, BEL I QUERALT, Germà and MAZAIRA-FONT, Ferran. Ensuring Stability, Accuracy and Meaningfulness in Synthetic Control Methods: The Regularized SHAP-Distance Method. IREA – Working Papers. 2020. Vol.  IR20/05. [consulted: 12 of June of 2026]. Available at: https://hdl.handle.net/2445/158714

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