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.
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.
Curso de Econometría con un aplicaciones en Python
Estimation, inference, RD Plots, and extrapolation with multiple cutoffs and multiple scores RD designs.
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 …
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