EconPapers    
Economics at your fingertips  
 

Bayesian inference for causal mechanisms with application to a randomized study for postoperative pain control

Michela Baccini (), Alessandra Mattei () and Fabrizia Mealli ()
Additional contact information
Michela Baccini: Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze, https://www.disia.unifi.it
Alessandra Mattei: Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze, https://www.disia.unifi.it
Fabrizia Mealli: Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze, https://www.disia.unifi.it

No 2015_06, Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"

Abstract: Principal stratification and mediation analysis are two ways to conceptualize the mediating role of an intermediate variable in the causal pathways by which a treatment affects an outcome. They are often viewed as competing frameworks, and their role in dealing with issues concerning causal mechanisms has often fired up glowing discussions. However a thoughtful comparative analysis, highlighting the substantive differences between the two frameworks is still lacking. We aim at filling this gap conducting both principal stratification and mediation analysis using, as a motivating example, a prospective, randomized, double-blind study to investigate to which extent the positive overall effect of treatment on postoperative pain control is mediated by postoperative self administration of intra-venous analgesia by patients. Using the Bayesian approach for inference, we estimate both associative and dissociative principal strata effects arising in principal stratification analysis, as well as natural effects and controlled direct effects from mediation analysis. We highlight that principal stratification and mediation analysis focus on different causal estimands, answer different causal questions and involve different sets of identifying assumptions. We discuss these aspects along the results arising from our analyses.

Keywords: Bayesian inference; Causal inference; Mediation analysis; Principal stratification; Oral morphine; Premedication, Postoperative pain, Potential outcomes; Randomized Experiments. (search for similar items in EconPapers)
Pages: 25 pages
Date: 2015-10
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://local.disia.unifi.it/wp_disia/2015/wp_disia_2015_06.pdf First version, 2015-10 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fir:econom:wp2015_06

Access Statistics for this paper

More papers in Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" Viale G.B. Morgagni, 59 - I-50134 Firenze - Italy. Contact information at EDIRC.
Bibliographic data for series maintained by Fabrizio Cipollini ().

 
Page updated 2020-11-28
Handle: RePEc:fir:econom:wp2015_06