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.

Costalli, S., Negri, F. (2021). Looking for twins: how to build better counterfactuals with matching. RIVISTA ITALIANA DI SCIENZA POLITICA, 51(2), 215-230 [10.1017/ipo.2021.1].

Looking for twins: how to build better counterfactuals with matching

Negri, F
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.
Articolo in rivista - Articolo scientifico
coarsened exact matching; Key words causation; peacekeeping; selection bias; statistical matching;
English
215
230
16
Costalli, S., Negri, F. (2021). Looking for twins: how to build better counterfactuals with matching. RIVISTA ITALIANA DI SCIENZA POLITICA, 51(2), 215-230 [10.1017/ipo.2021.1].
Costalli, S; Negri, F
File in questo prodotto:
File Dimensione Formato  
Lookingfortwins_CostalliNegri_2021.pdf

Solo gestori archivio

Descrizione: Articolo principale
Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 233.19 kB
Formato Adobe PDF
233.19 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/10281/358039
Citazioni
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
Social impact