Dr. Juan Camilo Orduz · Jun 10, 2023

Regression Discontinuity with GLMs and Kernel Weighting

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In this notebook we explore regression discontinuity design using generalized linear models (GLMs) and kernel weighting from a bayesian perspective. The motivation comes from applications when: 
 
 The data does not fit the usual linear regression OLS normal likelihood (e.g. modeling count data). 
 The data size is limited. 
 
 In addition, we experiment with kernel weighting…

In this notebook we explore regression discontinuity design using generalized linear models (GLMs) and kernel weighting from a bayesian perspective. The motivation comes from applications when:

The data does not fit the usual linear regression OLS normal likelihood (e.g. modeling count data).
The data size is limited.

In addition, we experiment with kernel weighting to weight the data points near the cutoff more heavily. This is a common technique in RD analysis, but it is not always clear how to do this with GLMs in the bayesian framework. We show how to do this with the PyMC.

Read on /regression_glmdiscontinuity_glm/ ↗

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