Real-Time Causal Inference

Abstract

The paper highlights causal inference based on econometric measurement in real-time data environments. Each state has a probability of being realized in real-time. We define state selection bias as arising when real-time environments are ignored. We model indicator variables as measurements that exist partly in all particular theoretically possible states, but show only one configuration on observation. Under real-time randomization within data streams, econometric treatment effects are 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 naively applied to real-time experimental data. We present a simple algorithm for difference-in-difference estimation for real-time program evaluations. Finally, a new Problem of Causal Inference is introduced for real-time data environments.