In this final post we outline the typical flow of a causal study in relations to the main tools provided by *Causalinference*. At a high level, assuming that unconfoundedness holds true in the given problem, we can break down a typical study into two phases.

# 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.

# Stratification

Unconfoundedness implies that matching on propensity scores also provides a valid way of constructing treatment effect estimators. In this post we look at a few ways of stratifying the sample into blocks that contain units with similar propensity scores...

Read More# Trimming

When there is indication of covariate imbalance, we may wish to construct a sample where the treatment and control groups are more similar than the original full sample. One way of doing so is by dropping units with extreme values of propensity scores...

Read More# Propensity Score

The probability of receiving treatment, also known as the propensity score, plays a very special role in the estimation of treatment effects. In this post we will consider how to estimate the propensity score...

Read More# Balance

Continuing with the simulated data set from the previous post, in this post we will look at some basic summary statistics, as well as assess the degree of covariate balance between the treatment and control groups...

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