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.

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

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

Curso de Econometría con un aplicaciones en Python

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

Course on Program Evaluation

Program Evaluation with Non-Parametric Statistics and Hypothesis Testing

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 …