Causal Inference One-to-one Matching on Confounders Using R

Using R Matching package for causal inference with Mahalanobis Distance Matching (MDM) in Google Colab notebook

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Causal inference is the process of determining the effect of a treatment. The causal impact can be evaluated by randomized experiments or observational studies.

• A randomized experiment randomly separates the samples into the treatment group and the control group. The causal impact can be calculated by getting the difference between the treatment group and the control group.
• When only the observational data is available, we can use causal inference algorithms to calculate the causal impact. Such algorithms include but are not limited to difference-in-difference, matching, Inverse Probability Treatment Weighting (IPTW), and counterfactual modeling.

Causal inference has well-established packages in R, but not in Python. This tutorial provides an example of using R packages for causal analysis in a Python notebook.

• If you are a Python user, please follow the steps in the tutorial to set up the environment to run the R package MatchIt for propensity score matching. To learn more about how to run R in a Python notebook, please check out my previous tutorial How to…