Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/1613
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DC Field | Value | Language |
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dc.contributor.author | Anandi Hira | |
dc.contributor.author | Barry Boehm | |
dc.contributor.author | Robert Stoddard | |
dc.contributor.author | Michael Konrad | |
dc.date.accessioned | 2020-03-16T17:59:51Z | - |
dc.date.available | 2020-03-16T17:59:51Z | - |
dc.date.issued | 2018-04-30 | |
dc.identifier.citation | Published--Unlimited Distribution | |
dc.identifier.uri | https://dair.nps.edu/handle/123456789/1613 | - |
dc.description | Acquisition Management / Defense Acquisition Community Contributor | |
dc.description.abstract | Correlation does not imply causation. Though this is a well-known fact, most analyses depend on correlation as proof of relationships that are often treated as causal. Causal search, also referred to as causal discovery, involves the application of statistical methods to identify causal relationships using conditional independences (and/or other statistical relationships) within data. Though software cost estimation models use both domain knowledge and statistics, to date, there has yet to be a published report describing the evaluation of a software dataset using causal search. In a previous paper, the authors ran a PC causal search algorithm on Unified Code Count's (UCC's) dataset of maintenance tasks and compared them to correlation test results. This paper builds on the previous paper to introduce causal discovery to software engineering research by exploring additional causal search algorithms (PC-Stable, fast greedy equivalent search [FGES], and fast adjacency skewness [FASK]) and comparing their results to the traditional multi-step regression analysis. | |
dc.description.sponsorship | Acquisition Research Program | |
dc.language | English (United States) | |
dc.publisher | Acquisition Research Program | |
dc.relation.ispartofseries | Acquisition Research | |
dc.relation.ispartofseries | SYM-AM-18-097 | |
dc.subject | Effort Estimation Data | |
dc.subject | Correlation | |
dc.subject | Causation | |
dc.subject | Causal Search | |
dc.subject | Statistical Methods | |
dc.subject | Unified Code Count | |
dc.subject | UCC | |
dc.subject | Fast Greedy Equivalent Search | |
dc.subject | FGES | |
dc.subject | Fast Adjacency Skewness | |
dc.subject | FASK | |
dc.title | Further Causal Search Analyses With UCC's Effort Estimation Data | |
dc.type | Article | |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Size | Format | |
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SYM-AM-18-097.pdf | 3.17 MB | Adobe PDF | View/Open |
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