What is causal inference? Why is it useful? How can you use to amplify your decision-making capabilities?
A casual introduction to causal inference for business analytics. Designed to help individuals without a statistics background leverage foundational key concepts in order to make data-driven decisions.
How do we represent causal relationships between the many interconnected processes which comprise our universe?
We’ve defined a language for describing the existing causal relationships between the many interconnected process that make up our universe. Is there a way we describe the extent of these relationships, in order to more wholly characterize causal effects?
When quantifying the causal effect of a proposed intervention, we wish to estimate the average causal effect this intervention will have on individuals in our dataset. How can we estimate average treatment effects and what biases must we be wary of when evaluating our estimation?
So far, our discussions of causality have been rather straightforward: we've defined models for describing the world and analyzed their implications. In this post I present the obstacles we may face when leveraging these models as well as the 'adjustments' we can make to remove them.
Oftentimes, analysts are interested how a particular intervention differentially affects an observed population. Exposure to a particular advertisement, experimental drug, or economic policy might affect different consumers in different ways. How can we estimate causal effects which vary across a population?