A Python package for causal inference using Synthetic Controls

Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.

Synthetic difference in differences for Python

📊 🌐 🧑‍🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto

📊 🌐 🧑‍🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto

Regression Discontinuity Design Software Packages

Manipulation testing using local polynomial density methods.

📊 🌐 🧑‍🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto

Finite-sample inference for RD designs using local randomization and related methods.

Curso de Econometría con un aplicaciones en Python

Estimation, inference, RD Plots, and extrapolation with multiple cutoffs and multiple scores RD designs.

Power and sample size calculations for RD designs using robust bias-corrected local polynomial inference.

syntCF is an R package that provides a set of tools to estimate the effect of a program or a policy using a robust time series synthetic counterfactual approach coupled with the double difference estimator within a Machine Learning framework.

A PyTorch implementation of the "robust" synthetic control model

Program Evaluation with Non-Parametric Statistics and Hypothesis Testing

Course on Program Evaluation

Home for Anthony D'Agostino's professional webpage

R Analysis + Code: Impact Evaluation Labor Market

We analyze two student outcomes – student GPA and average ACT test score for 1000 12th graders at the given school district. Specifically, we want to understand, among all the information we know about these students – demographics (gender, ethnicity, special education status, age, household income, and performance index), AP course taking, GPA …