A primary challenge for researchers that make use of observational data is selection bias (i.e. the units of analysis exhibit systematic differences and dis-homogeneities due to non-random selection into treatment). This article encourages researchers in acknowledging this problem and discusses how and – more importantly – under which assumptions they may resort to statistical matching techniques to reduce the imbalance in the empirical distribution of pre-treatment observable variables between the treatment and control groups. With the aim of providing a practical guidance, the article engages with the evaluation of the effectiveness of peacekeeping missions in the case of the Bosnian civil war, a research topic in which selection bias is a structural feature of the observational data researchers have to use, and shows how to apply the Coarsened Exact Matching (CEM), the most widely used matching algorithm in the fields of Political Science and International Relations.

Looking for twins: how to build better counterfactuals with matching / S. Costalli, F. Negri. - In: RIVISTA ITALIANA DI SCIENZA POLITICA. - ISSN 0048-8402. - (2021). [Epub ahead of print]

Looking for twins: how to build better counterfactuals with matching

F. Negri
Co-primo
2021

Abstract

A primary challenge for researchers that make use of observational data is selection bias (i.e. the units of analysis exhibit systematic differences and dis-homogeneities due to non-random selection into treatment). This article encourages researchers in acknowledging this problem and discusses how and – more importantly – under which assumptions they may resort to statistical matching techniques to reduce the imbalance in the empirical distribution of pre-treatment observable variables between the treatment and control groups. With the aim of providing a practical guidance, the article engages with the evaluation of the effectiveness of peacekeeping missions in the case of the Bosnian civil war, a research topic in which selection bias is a structural feature of the observational data researchers have to use, and shows how to apply the Coarsened Exact Matching (CEM), the most widely used matching algorithm in the fields of Political Science and International Relations.
causation; coarsened exact matching; peacekeeping; selection bias; statistical matching
Settore SPS/04 - Scienza Politica
2021
9-feb-2021
https://www.cambridge.org/core/journals/italian-political-science-review-rivista-italiana-di-scienza-politica/article/abs/looking-for-twins-how-to-build-better-counterfactuals-with-matching/D1BE1E399DB297F45D693D90302E8435
Article (author)
File in questo prodotto:
File Dimensione Formato  
Costalli_Negri_IPSR2021.pdf

accesso riservato

Tipologia: Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione 672.22 kB
Formato Adobe PDF
672.22 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
div-class-title-looking-for-twins-how-to-build-better-counterfactuals-with-matching-div.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 246.02 kB
Formato Adobe PDF
246.02 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/813319
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 3
social impact