While economists and social scientists have been using observational data for over a century for policy development, the business world has only recently been discovering the emerging potential of “big data” and “competing on analytics.” As these terms are becoming buzzwords, the observational nature of most “big data” sources is often overlooked. While the mantra of “correlation does not imply causation” remains frequently quoted as a general warning, many business analysts would not know under what specific conditions it can be acceptable to derive a causal interpretation from correlation in observational data. It is our objective to provide a framework that facilitates a more disciplined approach regarding causal inference while remaining accessible to (non-statistician) business analysts and transparent to executive decision makers. Download White Paper.
October 27, 12 noon CDT (GMT -05:00)
The adage, “I know I waste half of my advertising dollars...I just wish I knew which half”, reflects a century-old uncertainty about the effectiveness of marketing instruments. More formally, one could describe this quandary as a domain with an unknown (or ill-understood) structure.
While “big data”, especially in the field of marketing, is expected to rapidly yield “actionable business insights,” we need to recognize that there are many steps to traverse to achieve this goal. One crucial element is the formal transition from observational inference to causal inference.
In this webinar, Dr. Lionel Jouffe and Stefan Conrady will demonstrate the benefits of employing Bayesian networks as a robust framework to make the leap from observational data to causal reasoning.Register Here.
This webinar will cover many details of the new white paper on Causal Inference and Direct Effects with Bayesian Networks (see below).
Dr. Lionel Jouffe, Bayesia's co-founder and CEO, is scheduled to return to the U.S. in October 2011 to host a three-day training seminar on Bayesian Networks and introduces all the innovative features of the latest release of BayesiaLab. Dr. Jouffe will cover all the fundamentals of Bayesian Networks, so no prior knowledge is required other than a basic familiarity with mathematical and statistical concepts. Read more.
On October 15, the ACM San Francisco Bay Area Chapter will host the Data Mining Camp at the eBay campus in San Jose, CA. As in the previous year, Conrady Applied Science and BayesiaLab will once again support this event as a Gold Sponsor. Last year's Data Mining Camp was a hugely successful event with nearly 400 participants. For anyone interested in machine learning and knowledge discovery, this event is a must.
We utilize the unsupervised and supervised learning algorithms of the BayesiaLab software package to automatically generate Bayesian networks from daily stock returns over a six-year period. We will examine 459 stocks from the S&P 500 index, for which observations are available over the entire timeframe. In addition to generating human-readable and interpretable structures, we want to illustrate how we can immediately use machine-learned Bayesian networks as “computable knowledge” for automated inference and prediction. Our objective is to gain both a qualitative and quantitative understanding of the stock market by using Bayesian networks. In the quantitative context, we will also show how BayesiaLab can reliably carry out inference with multiple pieces of uncertain and even conflicting evidence. The inherent ability of Bayesian networks to perform computations under uncertainty makes them highly suitable for a wide range of real-world applications. Download White Paper.
BayesiaLab is a powerful desktop application (Windows/Mac/Unix) for knowledge discovery, data mining, analytics, predictive modeling and simulation - all based on the paradigm of Bayesian networks. Bayesian networks have become a very powerful tool for deep understanding of very complex, high-dimensional problem domains, ranging from biomedical research to marketing science. BayesiaLab is the world's only comprehensive software package for learning, editing and analyzing Bayesian networks. It provides perhaps the easiest way to practically apply artificial intelligence tools, thus transforming and, more importantly, massively accelerating research workflows. Read More.