An evolutionary algorithm for subset selection in causal inference models

Wendy K. Tam Cho

Journal of the Operational Research Society, 2018, vol. 69, issue 4, 630-644

Abstract: Researchers in all disciplines desire to identify causal relationships. Randomized experimental designs isolate the treatment effect and thus permit causal inferences. However, experiments are often prohibitive because resources may be unavailable or the research question may not lend itself to an experimental design. In these cases, a researcher is relegated to analyzing observational data. To make causal inferences from observational data, one must adjust the data so that they resemble data that might have emerged from an experiment. The data adjustment can proceed through a subset selection procedure to identify treatment and control groups that are statistically indistinguishable. Identifying optimal subsets is a challenging problem but a powerful tool. An advance in an operations research solution that is more efficient and identifies empirically more optimal solutions than other proposed algorithms is presented. The computational framework does not replace existing matching algorithms (e.g., propensity score models) but rather further enables and augments the ability of all causal inference models to identify more putatively randomized groups.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1057/s41274-017-0258-8 (text/html)
Access to full text is restricted to subscribers.

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:taf:tjorxx:v:69:y:2018:i:4:p:630-644

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1057/s41274-017-0258-8

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().