This is the second correspondence on last weeks Predictive Analytics World (PAW) in San Francisco. About a year and a half ago, I wrote a book review on Super Crunchers by Yale economist Ian Ayres, in which I noted that super crunching as the amalgam of predictive modeling and randomized experiments. Randomization to treatment and control groups allows investigators to minimize the risk of study bias so that the only important differences between groups out of the gate are that one is named treatment while the other is called control. Predictive modeling by itself allows analysts to infer relationships and correlation; the addition of experiments sharpens the focus to cause and effect. The combination of predictive modeling and experiments is thus a very potent tool in the business learning arsenal of hypothesize/experiment/learn.
All Information Management articles are archived after 7 days. REGISTER NOW for unlimited access to all recently archived articles, as well as thousands of searchable stories. Registered Members also gain access to:
- Full access to information-management.com including all searchable archived content
- Exclusive E-Newsletters delivering the latest headlines to your inbox
- Access to White Papers, Web Seminars, and Blog Discussions
- Discounts to upcoming conferences & events
- Uninterrupted access to all sponsored content, and MORE!