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ERIC Number: EJ1113256
Record Type: Journal
Publication Date: 2016-Oct
Pages: 18
Abstractor: As Provided
Reference Count: 30
ISBN: N/A
ISSN: ISSN-1076-9986
Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs
Mealli, Fabrizia; Pacini, Barbara; Stanghellini, Elena
Journal of Educational and Behavioral Statistics, v41 n5 p463-480 Oct 2016
Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded in a causal framework, can be found in concentration graphical models with a latent variable. The aim of this article is to establish a link between the two settings and to show that adapting and extending results pertaining to concentration graphical models can help achieving identification of principal casual effects in studies when more than one additional outcome is available. Model selection criteria are also suggested. An empirical illustrative example is provided, using data from a real social experiment.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A