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.

Blocking

With the sample divided into strata of units that have similar propensity scores, we should be able to get clean estimates of treatment effects within each block. Aggregating these estimates appropriately gives the blocking estimator, which we illustrate in this post...

Read More

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

Matching

One way to overcome the problem of excessive extrapolation of least squares involves directly executing on the unconfoundedness assumption and nonparametrically matching subjects with similar covariate values together. As we shall see, least squares still plays an important role under this approach, but its scope is restricted to being a local one...

Read More