To better illustrate some of the methods to be introduced presently, in this post we consider a simulated data set that exhibits a certain level of nonlinearity and covariate imbalance. In particular, consider the data generating process described below...

Read More# Causal Inference in Python

*Causal Inference in Python*, or *Causalinference* in short, is a software package that implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis.

Through a series of blog posts on this page, I 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. Source code for the package can be found at its GitHub page, and detailed documentation is available at causalinferenceinpython.org.

# Initialization

In this post we will go over the installation of *Causalinference*, as well as how to initialize the main class CausalModel...

# Setting

Whether an econometric methodology can yield good estimates of treatment effects depends crucially on the setting of the problem. The tools provided by *Causalinference* do not automatically yield causal estimates that work under every possible setting, so it is important to spell out the assumptions that we are operating under...

# Notation

In a nutshell, a causal effect is simply the difference between what happened and what would have happened. This notion is articulated more precisely by the so-called potential outcome framework, developed by Donald Rubin in the seventies. It goes as follows...

Read More# Introduction

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...

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