A Python package for causal inference using Synthetic Controls
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Updated
Jan 25, 2024 - Python
A Python package for causal inference using Synthetic Controls
Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.
📊 🌐 🧑🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto
Synthetic difference in differences for Python
📊 🌐 🧑🏫 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.
Finite-sample inference for RD designs using local randomization and related methods.
📊 🌐 🧑🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto
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.
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
Program Evaluation with Non-Parametric Statistics and Hypothesis Testing
R Analysis + Code: Impact Evaluation Labor Market
Home for Anthony D'Agostino's professional webpage
Course on Program Evaluation
Program Evaluation - Econometrics
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