dte_adj is a Python package for estimating distribution treatment effects. It provides APIs for conducting regression adjustment to estimate precise distribution functions as well as convenient utils.
Reproducible reanalysis of ISTDP vs. waitlist data for treatment-resistant depression using papaja and R
Code for the knapsack difficulty project
A package of wrapper functions that are useful when analyzing data from randomized controlled trials (RCTs, especially with three or more treatment assignments)
Tutorial on average marginal effects over time in a randomized controlled trial using R.
Presentation on principal component analysis with an application.
Randomized survey experiment testing whether "Limited Edition" labeling changes what consumers buy — a full causal-inference workflow (randomization & balance checks, ATE, FDR correction, power/MDE) in Python.
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