Causal Learning

Robert Stoddard
Robert Stoddard possesses 36 years of experience within software quality, reliability, design and test engineering within both defense and industrial settings, including Texas Instruments and Motorola. Robert has a lengthy history of leadership roles within both the American Society for Quality (ASQ) and IEEE Reliability organizations, including contributions to several books on six sigma for more


Abstract: Software reliability practice continues to evolve from a early focus on the modeling of software test failures for reliability estimation to the modeling of pre-test activities and software attributes for reliability prediction. The speaker believes the next major evolutionary step in software reliability research and practice will come with the application of causal learning. Causal learning has become a practical and exciting field rooted in matching methods employed long before Ronald Fisher created Designed Experimental methods in the 1930’s and 1940’s. This webinar will share the recently matured landscape of causal learning consisting of causal discovery and causal estimation. A brief description of causal methods, algorithms and modern publications will be shared along with recommendations on how reliability engineers might pursue learning and adopting causal learning.

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Previous Webinars
  • Seven Steps to Effective Problem Solving
    September 11, 2014
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  • Reliability DOE The Proper Analysis Approach for Life Data (可靠性实验设计: 寿命数据的正确分析方法)
    November 4, 2013
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