Using PROC DEEPCAUSAL to optimize revenue through policy evaluation

When it comes to causal inference, scoring capability is particularly beneficial. It can be used in unique ways that result in an improved decision-making process, such as gaining optimal revenue using the least number of resources. In this post, I will introduce to you a new scoring capability and its use cases with PROC DEEPCAUSAL. I will also show you how it utilizes Deep Neural Networks (DNNs) to perform causal inference as well as policy evaluation and comparison. Inference is not valid for the estimators when the estimates from machine learning methods are directly plugged into an econometric model. This way creates highly biased estimators, so econometrics methods need to correct for this bias.