Causal Models for Software Cost Prediction and Control (SCOPE)

Abstract

Why Causal Learning? Estimating and controlling program costs benefits from causal knowledge of program dynamics. Regression does not distinguish between correlation and causation. Causal knowledge is actionable knowledge. Causal discovery is becoming practical and is supported with innovative tools and algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2020
Accession Number
AD1110368

Entities

People

  • Bryar Wassum
  • David Zubrow
  • Michael D. Konrad
  • Michele Falce
  • Rhonda Brown
  • Robert W. Stoddard
  • William R. Nichols

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artifacts
  • Causal Reasoning
  • Cost Estimates
  • Costs
  • Data Sets
  • Department Of Defense
  • Engineering
  • Lessons Learned
  • Materials
  • Reliability
  • Software Development
  • Statistics
  • Sustainment
  • Systems Engineering
  • Transitions
  • Universities

Fields of Study

  • Computer science

Readers

  • Aviation Safety Risk Assessment.
  • Distributed Systems and Data Platform Development
  • Life Cycle Cost Analysis