Resources | Hong Lee

Christine Cai’s resources of applied econometrics, PhD-level class materials, coding in Stata and R (https://christinecai.github.io/items/PublicGoods.html)

Seeing Theory A visual intoduction to probablit and statistics: (https://seeing-theory.brown.edu/)

The recordings of our Symposium on the Economics and Law of Pharmaceutical Regulation: (https://vimeo.com/showcase/8070909)

What are the most important statistical ideas of the past 50 years?: (http://www.stat.columbia.edu/~gelman/research/unpublished/stat50.pdf)

All the codes needed for applied econometrics: (https://sites.google.com/site/waynelinchang/r-code)

Masayuki Kudamatsu’s Tips for economist to apply phd in economics, proceed life with Phd, choosing research topic: (https://sites.google.com/site/mkudamatsu/tips4economists)

NickHK education videos: (https://www.nickchk.com/videos.html)cy

BREAD-IGC Virtual Ph.D. Course, Spring 2022 (https://www.theigc.org/event/bread-igc-virtual-phd-course-spring-2022/)

Econometrics Academy(https://sites.google.com/site/econometricsacademy/)

Ivan Canay’s Econometrics course ECON 480 and ECO 481

Applied Econometrics at NYU Stern: (https://github.com/chrisconlon/applied_metrics)

Machine Learning by Jordan Boyd-Graber: (https://home.cs.colorado.edu/~jbg/teaching/CSCI_5622/)

A history of econometric debates: (https://twitter.com/Undercoverhist/status/1105851715461570560)

Books

Geocomputation with R and Python

Econometric data science, forecasting, and time series by Francis X. Diebold

Handbook of Regression Modeling in People Analytics by Keith McNulty

Structural Bayesian Techniques for Experimental and Behavioral Economics by James Bland

Data Management in Large-Scale Education Research By Crystal Lewis

Chicago Price Theory with video

Introduction to Geographic Data Science by Francisco Rowe codes

[Introduction to Data Science by Rafael A. Irizarry] (http://rafalab.dfci.harvard.edu/dsbook/)

Statistical Tools for Causal Inference by Sylvain Chabé-Ferret

Tidy Finance book by Christoph Scheuch, Stefan Voigt, and Patrick Weiss

Data Analysis for Business, Economics, and Policy, by Gábor Kézdi

Tidy Modeling with R by Max Kuhn and Julia Silge

Geocomputation with R

Stacked tilted maps

Computational Thinking for Social Scientists

Applied Economics with R by Hans H. Sievertsen

Introduction to R by Hans H. Sievertsen

The Effect: An Introduction to Research Design and Causality, by Nick Huntington-Klein and data resources

Causal Inference: The Mixtape, by Scott Cunningham

Crime by the Numbers: A Criminologist’s Guide to R, by Jacob Kaplan

Models in Microeconomic Theory (‘She’ Edition), by Martin J. Osborne and Ariel Rubinstein

Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt, Julia Silge

Introduction to Probability for Data Science by Stanley H. Chan

Mastering Shiny by Hadley Wickham

Engineering Production-Grade Shiny Apps by Colin Fay, Sébastien Rochette, Vincent Guyader and Cervan Girard

Advanced R by Hadley Wickham

Shiny tips & tricks for improving your apps and solving common problems

Doing Economics

Causal Inference for The Brave and True

Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker

Big Book of R which lists all the book avialable using R

EconML User Guide

Data Science at the Command Line Obtain, Scrub, Explore, and Model Data with Unix Power Tools

Mastering Spark with R by Javier Luraschi, Kevin Kuo, Edgar Ruiz

Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari

Courses for causal inference

Golden comments on Double Machine Learning

ML Based Causal Inference

Free Causal Inference Resources BY Matteo Courthoud

Introduction to Modern Causal Inference By Alejandro Schuler Mark van der Laan

Which causal inference book you should read: A flowchart and a list of short book reviews: (https://www.bradyneal.com/which-causal-inference-book)

Miguel Hernán’s Causal Diagrams: Draw Your Assumptions Before Your Conclusions: (https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your)

UPenn’s course: A Crash Course in Causality: Inferring Causal Effects from Observational Data: (https://www.coursera.org/learn/crash-course-in-causality)

Columbia University’s Causal Inference-I (https://www.coursera.org/learn/causal-inference) and Causal Inference-II (https://www.coursera.org/learn/causal-inference-2)

Andrew Heiss’s course materials from Andrew Young School of Policy Studies at Georgia State University: Program Evaluation for Public Service, Microeconomics for Public Policy, and Data Visualization (https://www.andrewheiss.com/teaching/)

Data science for economists: (https://github.com/uo-ec607/lectures)

Ben Elsner’s Causal Inference – Online Lectures (https://www.youtube.com/playlist?list=PLyvUJLHD8IsJCB7ALqwjRG1BjL5JxE__H)

Brady Neal - Causal Inference (https://www.youtube.com/watch?v=DXBPtpBhGqo&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0)

Online Causal Inference Seminar: (https://sites.google.com/view/ocis/)