A central problem of interest to social scientists, businesses, and government agencies alike is the evaluation of the effects of a treatment or policy intervention. Causal Inference is the statistical field of study concerning this problem, and it boasts a mature literature that contains numerous sophisticated methodology and recommendations.

Unfortunately, much of this literature has so far proved difficult to access for non-specialists due to its technical nature and the high implementation costs associated with the more advanced methods. The software package *Causal Inference in Python*, or *Causalinference* in short, is an attempt to bridge this gap by exposing the statistical tools in an easy-to-use interface.

This series of blog posts will illustrate the use of *Causalinference*, as well as provide high-level summaries of the underlying econometric theory with the non-specialist audience in mind. For those who are already familiar with the literature, I recommend referring to the much terser vignette paper. These blog posts can be thought of as an expanded but less formalized version of that material.