How can an analyst 'control' for a large number of confounding variables when they are analyzing causal effects in an observational setting, and have very little control over which individuals receive which treatment?
A presentation of a variety of causal inference methods as well as the business case scenarios for which they can be the most effective.
What are some of the challenges an analyst must be wary of when using propensity score matching for causal inference tasks?
Analysts are often interested how a particular intervention differentially affects individuals within an observed population, given high dimensional data describing each individual's characteristics. In this scenario, what state of the art machine learning technique is best suited for estimating heterogeneous treatment effects?