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Applied Econometrics / Causal Inference / ML Notes

Published: September 04, 2023

Theory

Computing

Published: September 04, 2023

Unix snippets for reference

Statistical Computing in Python and R

Published: September 04, 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

R&R Journal of Historical Political Economy, 2020

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

Shirking Paper, 2021

Improved Variable Selection for Regression and Balancing Estimators

Working Paper, 2021

Multi-Armed Bandits for Budget-Constrained Data Collection

MIT Conference on Digital Experimentation (CODE), 2022

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

MIT Conference on Digital Experimentation (CODE), 2022

with Wenjing Zheng, Simon Ejdemyr

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

American Political Science Review, 2023

with Saad Gulzar, Ben Pasquale

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

R&R Political Analysis, 2023

with Mac Lockhart, Yiqing Xu, Ziwen Zu

The Electoral Consequences of Cellphone Coverage Expansion

R&R, 2023

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

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

Working Paper, 2023

Asymmetries in Cricket

Accepted, Journal of Sports Analytics, 2023

with Gaurav Sood, Avi Acharya, Derek Willis

Augmented Balancing Estimators for the Average Treatment Effect on the Treated in Cross-Sectional and Panel Designs

Working Paper, 2023

Did Private Election Administration Funding Advantage Democrats in 2020?

Working Paper, 2023

with Daniel M Thompson

Reproducible Geospatial Analysis in Python

Published: November 15, 2019

slides

notebook

repo

PhD Causal Inference and Machine Learning

Published: September 04, 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: September 04, 2023

Panel Data

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