Hi😊 I’m Jared Amani Greathouse. Currently, I’m a public policy PHD candidate at Georgia State and the Georgia Institute of Technology, where I am fortunate to be advised by Jason Coupet. Prior, I earned my Bachelors and Masters in political science from Georgia State University. I am the author of the mlsynth Python package, which implements various recent machine-learning and proximal causal inference techniques such as the l2-relaxer or the [Bayesian] Robust Synthetic Control.

I am an econometrician specializing in causal inference, particularly with the synthetic control method and difference-in-differences. My current work focuses on high-dimensional data and machine learning methods for treatment effect estimation. If causal inference is your thing, check out the software I develop for Stata and Python. I am also an active contributor to the Statalist forum. Here’s my vita. If you’d like to talk with me about my work, my consulting services, or anything else that’s of interest, you may always reach out to me!

• Email: jgreathouse3[at]student[dot]gsu[dot]edu

Here is a plot generated via GitHub Actions, a tool we may use to automate data collection/repetitive tasks:

• Here is a dashboard of scraped Spotify data I developed with Streamlit.

News

• As of January 3, 2025, the alpha version of my Python package mlsynth is operational. The documentation is stil being created, but tutorials exist for all but two classes.

• As of October 6th, 2024, my draft of an empirical replication is available, where I compare and contrast the findings of the Forward DID method versus the fsPDA, using the Chinese Anti-Corruption Campaign as an example. Here is the replication code.

• As of September 27th, my draft for the Forward DID method is avaliable. Here is the link to read it. Note that it is has not been submitted for review yet. Any comments or feedback would be appreciated.