Causal Inference Using Potential Outcomes for a General Assignment Scheme

Rahul Mukherjee ()
Additional contact information
Rahul Mukherjee: Indian Institute of Management Calcutta

No 4607283, Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences

Abstract: Recent years have witnessed a significant surge of interest in causal inference under a potential outcomes framework, with applications to such diverse fields as sociology, behavioral sciences, biomedical sciences, and so on. In a finite population setting, we consider causal inference on treatment contrasts from an experimental design using potential outcomes. Adopting an approach that integrates such causal inference with finite population sampling, this is done with reference to a general scheme for assigning experimental units to treatments, along with general linear unbiased estimators of the treatment means. The assignment scheme allows the possibility of randomization restrictions, such as stratification, and unequal replications. We examine how tools from finite population sampling can be employed to develop a unified theory for our general setup. As a major breakthrough, it is shown that unbiased estimation of the sampling variance of any treatment contrast estimator is possible under conditions milder than Neymannian strict additivity. The consequences of departure from such conditions are also touched upon. Our approach applies readily to the situation where the treatments have a general factorial structure.

Keywords: Finite population sampling; linear unbiased estimator; Neymannian strict additivity; variance estimation. (search for similar items in EconPapers)
JEL-codes: C13 C83 C90 (search for similar items in EconPapers)
Pages: 1 page
Date: 2017-07
New Economics Papers: this item is included in nep-ecm and nep-exp
References: Add references at CitEc
Citations:

Published in Proceedings of the Proceedings of the 31st International Academic Conference, London, Jul 2017, pages 158-158

Downloads: (external link)
https://iises.net/proceedings/31st-international-a ... =46&iid=035&rid=7283 First version, 2017

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:sek:iacpro:4607283

Access Statistics for this paper

More papers in Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences
Bibliographic data for series maintained by Klara Cermakova ().