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Title: Further Causal Search Analyses With UCC's Effort Estimation Data
Authors: Anandi Hira
Barry Boehm
Robert Stoddard
Michael Konrad
Keywords: Effort Estimation Data
Causal Search
Statistical Methods
Unified Code Count
Fast Greedy Equivalent Search
Fast Adjacency Skewness
Issue Date: 30-Apr-2018
Publisher: Acquisition Research Program
Citation: Published--Unlimited Distribution
Series/Report no.: Acquisition Research
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.
Description: Acquisition Management / Defense Acquisition Community Contributor
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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