Causal inference with heavily skewed continuous variables in Google Cloud

Music City Center 

A/B testing is the golden standard to measure causal relationship, but it can be operationally infeasible in certain business scenarios. Alternatively, causal inference is critically used in Google Cloud to quantify the business impact of new releases or launches. In practice, the success of an attribution analysis depends on accurately identifying the key variables and representing them in the most suitable forms, which often result in various variable types. In this talk, we consider the case when continuous variables are in presence, more specifically, when the covariates contain continuous variables, and when the response of interest is continuous. We will discuss the main challenges from heavily skewed continuous variables, and a simple solution to incorporate them into a doubly-robust causal framework widely adopted within Google Cloud.

Causal inference

Doubly robust

Machine learning

Continuous variables

Skewed variables

Covariate balance 

Section on Statistical Learning and Data Science