Teaching/Resources

I have helped teach an undergraduate data analysis class focused on the causes and consequences of political corruption and a graduate course on causal inference. Below are some relevant materials I have collected and presented that may be of use to others. I also include some tutorials I have developed for presentation to graduate students in the UCLA Political Science department.

Causal inference

Machine Learning Estimation of Heterogeneous Treatment Effects

A short tutorial on how to implement in R a simple version of a data-driven approach to uncovering heterogeneous treatment effects proposed by Athey and Imbens (2015). The tutorial can be found here.

An official package has now been released by Susan Athey and friends and can be found here.

Summarizing TIFF Data by Polygons in Shapefiles

A brief introduction to how to trim and spatially summarize data in tiff format by polygon shapefiles. Useful for averaging night time luminosity data by political district, for example.

Downloading Facebook and Twitter Data using Python

Put together for a workshop on January 29, 2016, this tutorial and accompanying example scripts provide a guide to downloading Facebook and Twitter data using their APIs. Done entirely in Python, this tutorial takes advantage of several Python modules to ease data collection and storage.

LUMS R Course

Class .Rmd Files

Class .R files

Datasets

Quality of Government

Pakistan 2018 candidate level election data

CricInfo batting data

PK Supreme Court Survey