Topics in Econometrics: Advances in Causality and Foundations of Machine Learning

Syllabus

Syllabus Harvard, fall 2019

Syllabus Brown, fall 2019

Slides

• Introduction to R Slides

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• Instrumental variables part I – origins and binary treatment Slides
• Instrumental variables part II – continuous treatment Slides
• Guest lecture Isaiah Andrews: Weak instruments Slides

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• Review of decision theory Slides
• Shrinkage in the normal means model Slides
• Gaussian process priors, reproducing kernel Hilbert spaces, and Splines Slides
• Applications of Gaussian process priors from my own work Slides

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• Text analysis Slides
• Regression trees and random forests Slides
• Deep neural nets Slides
• Bandit problems Slides
• Reinforcement learning Slides
• Data visualization Slides

Homework problems

• Problemset 1 Ec2148-2019-PS1.pdf
• Problemset 2 Ec2148-2019-PS2.pdf

Reading materials

Papers

• Identification and instrumental variables Identification-IV-readings.zip
• Shrinkage and Gaussian Processes Shrinkage-readings.zip
• Text, forests, neural nets, bandits, reinforcement learning, and visualization DeepNets-Text-Bandits-Visualization-readings.zip

Machine learning books

• The Elements of Statistical Learning
• Gaussian Processes for Machine Learning
• Deep Learning
• Reinforcement learning - An introduction

Programming in R

• An Introduction to R
• Advanced R
• Bayesian statistics using Stan
  RStan
• Training neural nets using Keras and Tensorflow

Data visualization

• Data Visualization - A Practical Introduction
• An Economist’s Guide to Visualizing Data
• ggplot2 - Elegant Graphics for Data Analysis