Engineers often need to understand how to deploy new innovations to maximize impact in real-time environments. For collaborations to succeed, researchers must understand and communicate statistical causal inference in ways that are consistent with unstructured settings where estimates can change in real-time. In my practice, I have found it useful to draw linkages between statistical and engineering concepts. The paper highlights several areas on statistical measurement and causal inference in common real-time data environments. Treatment effects under real-time randomization within data streams are found estimable using controlled and natural experiments motivated by real-time regression analyses. A bias occurs as a result of ignoring concept drift when classical regression statistics are naïvely applied to real-time experimental data. An algorithm performs difference-in-difference estimation in real-time. A new Problem of Causal Inference emerges for real-time data environments. Such approaches can help statisticians and engineers engage in fruitful discussions and collaborations for mutual benefit.