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Applied Econometrics / Causal Inference / ML Notes

Published: January 27, 2023

Theory

Computing

Published: January 27, 2023

Unix snippets for reference

Statistical Computing in Python and R

Published: January 27, 2023

Notebooks and reference for most routine tasks in data-management and econometrics in R and Python [typically written in jupyter notebooks / forked and exported with to HTML H1-H4 headers for easy reference using the html-toc extension].

Land Tenure and Missing Women: Evidence from North India

Under Review, 2019

Representation and Forest Conservation: Evidence from India’s Scheduled Areas

Working Paper, 2020

with Saad Gulzar, Ben Pasquale

Political Consequences of Youth Franchise Extension: Evidence from India’s 61st Amendment

Under Review, 2021

How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice based on Over 60 Replicated Studies

Under Review, 2021

with Mac Lockhart, Yiqing Xu, Ziwen Zu

Multi-Armed Bandits for Budget-Constrained Data Collection

MIT Conference on Digital Experimentation (CODE), 2021

Improved Variable Selection for Regression and Balancing Estimators

Working Paper, 2021

The Electoral Consequences of Cellphone Coverage Expansion

Under Review, 2021

with Shuning Ge, Guy Grossman, Katrina Kosec, Benjamin Laughlin

Tobler meets Robinson: Semiparametric methods for Covariate Adjustment using Spatial Data

Working Paper, 2022

Asymmetries in Cricket

Working Paper, 2022

with Gaurav Sood, Avi Acharya, Derek Willis

A Framework for Generalization and Transportation of Causal Estimates under Covariate Shift

MIT Conference on Digital Experimentation (CODE), 2022

with Wenjing Zheng, Simon Ejdemyr

Reproducible Geospatial Analysis in Python

Published: November 15, 2019

slides

notebook

repo

PhD Causal Inference and Machine Learning

Published: January 27, 2023

450B: Causal Inference (W:2021,2022)

TA for Prof Jens Hainmueller

• Visualising identification strategies
• Slides
  • Randomised Experiments
  • Selection on Observables
  • Sensitivity Analysis
  • Difference in Differences
  • Panel Data and Synthetic Control
  • Instrumental Variables I
  • Instrumental Variables II
  • Regression Discontinuity Design

450C: Machine Learning (S:2021,2022)

TA for Prof Justin Grimmer

• Slides
  • Maximum Likelihood Basics
  • Goodness of Fit, Principal Components Analysis
  • Regularised Regression
  • Random Forests and GAMs
  • Text Analysis and LDA
  • Heterogeneous Treatment Effects
    • More advanced treatment for a workshop
  • ML and Causal Inference under Unconfoundedness
    • More advanced treatment including material on continuous treatments and panel data

Reading Group Slides

Published: January 27, 2023

Panel Data

• Doudchenko and Imbens (2016)
  • arxiv
• Imbens, Kallus, Mao (2021)
  • arxiv