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Elder Research is a recognized leader in the science, practice, and technology of advanced analytics. Topics on our blog cover analytics tips, analytical modeling, data and text mining tools, data visualization, analytics best practices, case studies, etc. to provide business leaders with actionable information on real world analytics problems.
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Measuring Invisible Treatment Effects with Uplift Analysis: A Get-Out-The-Vote ExampleModels make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk of heart disease as we age is actionable. Knowing millennials are more likely to buy your product than gen Z is nice, but knowing which marketing approach will...
Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk of heart disease as we age is actionable. Knowing millennials are more likely to buy your product than gen Z is nice, but knowing which marketing approach will persuade gen Z to buy is valuable. In this election season, knowing who will vote is interesting, but identifying unlikely voters who can be persuaded to show up at the polls is everything to campaign managers. When data science goes further to estimate the impact of alternative actions we may perform to achieve a better outcome, we call it uplift modeling or, more technically, treatment effect modeling. For this instructional blog we will use a very limited example of how uplift modeling can apply to get-out-the-vote campaigns, without divulging which sample or geography was used.
Building a Successful Analytics Project from the Ground Up
Like a house built to withstand the seasons, a successful and sustainable analytics project must start with a firm foundation, a purposeful plan, a seasoned team, and the right tools and materials. Analytics project owners and budget-holders, much like a homeowner, want to see their large investment result in something that delivers value and supports evolving...
Like a house built to withstand the seasons, a successful and sustainable analytics project must start with a firm foundation, a purposeful plan, a seasoned team, and the right tools and materials. Analytics project owners and budget-holders, much like a homeowner, want to see their large investment result in something that delivers value and supports evolving needs.
Creating a Legacy as a Chief Data Officer by Optimizing the Value of Data for Public Good
As the Chief Data Officer (CDO) role gets established at each federal agency as required by the Evidence-Based Policymaking Act of 2018 (Public Law 115-435) (Evidence Act), I can’t help but feel conflicted. On one hand, I am excited that the federal government has taken such an intentional step towards a more data-driven government: There are enormous potential benefits when CDOs partner with program owners to innovate and deliver real values to the federal government and our citizens. On...
As the Chief Data Officer (CDO) role gets established at each federal agency as required by the Evidence-Based Policymaking Act of 2018 (Public Law 115-435) (Evidence Act), I can’t help but feel conflicted. On one hand, I am excited that the federal government has taken such an intentional step towards a more data-driven government: There are enormous potential benefits when CDOs partner with program owners to innovate and deliver real values to the federal government and our citizens. On the other hand, I am concerned this role, if not properly supported and empowered, could become yet another silo yielding little real value.
New Tool Helps States Reduce Unemployment Insurance Overpayment
Elder Research has extensive experience helping enterprises find and eliminate fraud, waste and abuse (FWA). For several state-level labor departments, we have assisted with identifying fraudulent unemployment insurance (UI) claims. This capability is critical today, during the unfortunate record-setting unemployment over the last few months. This blog is dedicated to help agencies – who are wary of domestic and foreign abusers of the UI system – by describing our successful work, where...
Elder Research has extensive experience helping enterprises find and eliminate fraud, waste and abuse (FWA). For several state-level labor departments, we have assisted with identifying fraudulent unemployment insurance (UI) claims. This capability is critical today, during the unfortunate record-setting unemployment over the last few months. This blog is dedicated to help agencies – who are wary of domestic and foreign abusers of the UI system – by describing our successful work, where and how it is being used, and how to leverage the lessons learned and tools built to identify FWA.
Policy Impact on COVID-19 Spread
As COVID-19 began to take off in the U.S. early this year, federal, state, and county governments hurried to contain it. Policies restricting movement, closing businesses, etc., have become a staple of government policy, including many stay-at-home directives across the U.S. These measures have been met with varying levels of enthusiasm, but they are rooted in an understanding about how epidemic diseases spread. Now, with several months of data, we can begin to test whether these policies...
As COVID-19 began to take off in the U.S. early this year, federal, state, and county governments hurried to contain it. Policies restricting movement, closing businesses, etc., have become a staple of government policy, including many stay-at-home directives across the U.S. These measures have been met with varying levels of enthusiasm, but they are rooted in an understanding about how epidemic diseases spread. Now, with several months of data, we can begin to test whether these policies have been effective in their primary function and to what extent a controlled “reopening” raises a community’s COVID-19 risk profile.
AUC: A Fatally Flawed Model Metric
The blog Recidivism, and the Failure of AUC published on Statistics.com showed how the use of “Area Under the Curve” (AUC) concealed bias against African-Americans defendants in a model predicting recidivism, that is, which defendants would re-offend. There, a model varied greatly in its performance characteristics depending on whether the defendant was white or black. Though both situations resulted in virtually identical AUC measures, they led to very different false alarm vs. false...
The blog Recidivism, and the Failure of AUC published on Statistics.com showed how the use of “Area Under the Curve” (AUC) concealed bias against African-Americans defendants in a model predicting recidivism, that is, which defendants would re-offend. There, a model varied greatly in its performance characteristics depending on whether the defendant was white or black. Though both situations resulted in virtually identical AUC measures, they led to very different false alarm vs. false dismissal rates. So, AUC failed the analysts relying on it, as it quantified the wrong property of the models and thus missed their vital real-world implications.
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