Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables

2SLS Regression with Diagnostics in R

R package of useful functions for one-sample Mendelian randomization and instrumental variable analyses

CRAN Task View: Causal Inference

nl-causal: nonlinear causal inference based on IV regression in Python

Inference in SVMA models identified by external instruments/proxies

A Stata module for an instrumental variables correlated random coefficients estimator.

Investigate how racial segregation influences urban poverty and inequality, using railroad track layouts as an instrumental variable (IV) to estimate the causal impact of segregation on poverty rates.

This is the website repository for the Stata packages lassopack & pdslasso. Please visit:

Analyze the effects of seasonal migration on household consumption and expenditures in rural Bangladesh from a randomized control trial.

Lecture slides, video recordings, and coding exercises from the 2024 Northwestern University Causal Inference Workshop. This repository is not affiliated with Northwestern University or the workshop.

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

R code to reproduce results of data examples in 'Selecting invalid instruments to improve MR with two-sample summary data''

Stata package for one-sample Mendelian randomization / instrumental variable analyses

2SLS IV regression with Python

This repository examines how EV charging stations affect the adoption of plug-in hybrid and battery electric vehicles in the U.S. using causal inference and instrumental variable methods to evaluate the NEVI Formula Program's effectiveness.

Code for an R package to implement instrumental variables procedures that are robust to many & weak instruments.

R Code That Makes up the Bulk of my Master's Paper

This assignment relates to my 8th assignment in Econ 323 - Econometrics Analysis 2. I generate new demand and supply data and use this to test different casualty methods such as TSLS, IV, and Diff-In-Diff.