About

We started this project to share our knowledge and expertise on data science concepts and practices from two different perspectives: academic and practice. One of the authors, Gorkem Turgut (G.T.) Ozer, is a decision sciences professor currently at the Paul College of Business and Economics, where his research and teaching focus on causal modeling using statistics, machine learning, and deep learning in big data. The second author, Duygu Dagli, is a data science and pricing executive who currently serves as Director of Data Science - Pricing & Promotions Strategy. Each article concludes with a discussion of data centricity. You can learn more about data centricity at datacentricity.org.

Other popular articles

How to (and not to) log transform zero

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Image courtesy of the authors: Survey results of the papers with log zero in the American Economic Review Academic's take Log transformation is widely used in linear models for several reasons: Making data "behave" or conform to parametric assumptions, calculating elasticity, etc. The figure above shows that nearly 40% of the empirical papers in a selected journal used a log specification and 36% had the problem of the log of zero. When an outcome variable naturally has zeros, however, log transformation is tricky. In most cases, the solution is to instinctively add a positive constant to each value in the outcome variable. One popular idea is to add 1 so that raw zeros remain as log-transformed zeros. Another is to add a very small constant, especially if the scale of the variable is small. Well, the bad news is that these are all arbitrary choices that bias the resulting estimates. If a model is correlational, a small bias due to the transformation may not be a big conc
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What if parallel trends are not so parallel?

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Image courtesy of github.com/asheshrambachan/honestdid Academic's take In difference-in-differences models, parallel trends are often treated as a make-or-break assumption, and the assumption is not even testable. Why not? One misconception is that comparing the treated and control units before treatment is a test of the assumption. In reality, the assumption is not limited to the pretreatment period but for the entire counterfactual. Comparing trends in the pretreatment period is only a plausibility test. That is, "parallel pretreatment trends" and "parallel counterfactual trends" are not the same. We can observe the former, but we need the latter. So, what we have is not what we want, as is usually the case. Even testing for parallel pretreatment trends alone is tricky because of potential power issues (evidence of absence is not the absence of evidence!) and other reasons, including a potential sensitivity to the choice of functional form / transformations (
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Synthetic control method in the wild

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Image courtesy of dataingovernment.blog.gov.uk Academic's take Synthetic data is increasingly popular across the board. From deep learning to econometrics, artificially generated data is used for a number of purposes. In deep learning, one such use case is to train neural network models using artificially generated data. In econometrics, synthetic data has recently found another use case: creating control groups in observational studies for causal inference . Synthetic data + control groups: Synthetic controls. This is too generic of a name for a specific method. In this post, I will focus on the synthetic control approach developed by Abadie et al. (2010, 2014) and previously Abadie & Gardeazabal (2003). Athey and Imbens (2017) describe Abadie et al.'s work as "arguably the most important innovation in the policy evaluation literature in the last 15 years." Why is it needed? Measuring the causal effect of a treatment requires a counterfactual (what would've h
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