Causal Inference with Observational Data: A Tutorial on Propensity Score Analysis

Kaori Narita, Juan de Dios Tena and Claudio Detotto

No 202225, Working Papers from University of Liverpool, Department of Economics

Abstract: When treatment cannot be manipulated, propensity score analysis provides a practical approach to making causal claims. However, it is still rarely utilised in leadership and applied psychology research. The purpose of this paper is threefold. First, it explains and discusses the application of the method with a particular focus on propensity score weighting. This approach is readily implementable since a weighted regression is available in most statistical software. Moreover, using a double robust estimator can offer protection against the misspecification of the model by including confounding variables both in the treatment and response equations. A second aim is to discuss how propensity score analysis has been conducted in recent management studies and examine future challenges. Finally, we illustrate the method by showing how it can be employed to estimate the causal impact of leadership succession on performance using data from Italian football. The case also exemplifies how to extend the standard single treatment analysis to estimate the separate impact of different managerial characteristic changes between the old and the new manager.

Keywords: causality; propensity score; leadership succession; observational data; football (search for similar items in EconPapers)
JEL-codes: C31 J24 J63 M51 Z22 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2022-11
New Economics Papers: this item is included in nep-ecm
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Forthcoming

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https://www.liverpool.ac.uk/media/livacuk/schoolof ... y,Score,Analysis.pdf First version, 2022 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:liv:livedp:202225

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