People don’t always agree; that is a fact of life. Similarly, when running an experiment, not everyone has the same reaction to the intervention! It’s critical that data scientists, academics, and the general public understand that the global average may not always be the most important or meaningful measure. Instead, it is often more informative to study how the effect of an intervention varies across different population subgroups. This post explains, at a high level, what heterogeneous treatment effects are, why they are essential, and how to think about them.
Much of the traditional results in causal inference rely on a common assumption — that the treatment assigned to a unit does not affect the outcomes of other units. This so-called “no interference” assumption is often untenable in a connected world in which units interact with each other. This post gives a very high-level introduction to the problem of interference.
The Potential Outcomes Framework (aka the Neyman-Rubin Causal Model) is arguably the most widely used framework for causal inference in the social sciences. This post gives an accessible introduction to the framework’s key elements — interventions, potential outcomes, estimands, assignment mechanisms, and estimators.