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README.md

Machine Learning for Social Science

Applying machine learning with examples and concerns of social scientists, focusing on causal inference, estimating uncertainty, robustness and policy applications.

Instructor

Andrew Peterson (Postdoctoral Researcher, University of Geneva)

Course Description

This course is designed with social scientists in mind, by which it is meant simply that it assumes an interest in certain concerns that are not always common among computer scientists and others who do machine learning. While I hope the course may be useful more broadly, it will be of particular interest to social scientists due to a focus on the following areas:

  1. Assumptions and Research Design We will compare the approach and assumptions of machine learning to the econometric approach more familiar to political scientists and economists.
  2. Causal Inference We will also approach the use of machine learning with the causal inference framework in mind, and think about how machine learning may add additional problems, or help in analyzing data with testing theories and examining relationships that may be plagued by selection bias.
  3. Applications The examples used and applications examined will be drawn from social science research.

Prerequisites

This course assumes basic familiarity with machine learning approaches and algorithms as well as econometrics (although these are reviewed in Part 1). Alternatively, you may review the following:

Econometrics

You should be familiar with the following:

  • hypothesis testing, multiple regression, the Gauss-Markov theorem,
  • heteroskedasticity, clustered and auto-correlated errors
  • binary outcome models
  • commonly-used approaches such as how to code categorical variables, interpret interaction effects, partialling out, etc.

Machine Learning

If you are not already familiar with machine learning, I suggest the following readings from The Elements of Statistical Learning, 2nd Ed as most essential to the material:

  1. Fundamentals of Machine Learning (Chs 2, 7)
  • the curse of dimensionality
  • machine learning as function approximation
  • bias-variance tradeoff
  • regularization
  • cross-validation
  • model selection
  1. Linear Methods for Regression (Ch 3)
  • Subset selection, Lasso, ridge regression
  1. Additive Models, Boosting, Trees and Forests (Chs 9, 10, 15)

  2. Model Inferece

  • Bootstrap
  • Maximuml Likelihood
  • EM
  • Bagging
  1. Unsupervised learning (Ch 14)
  • Focus on PCA (14.5) and Multidimensional Scaling (14.8)

Course Outline

Content Materials
1 Fundamentals, Assumptions Slides
2 Comparing ML and Econometric Approaches Slides
3 ML for Causal Inference I: Identification, double robustness in high-dimensions Slides
4 ML for Causal Inference II: IV, effect heterogeneity, optimal treatment assignments
5 Applied Example 1: ML and Measuring Polarization Slides
6 Applied Example 2: Deep Learning and Texts Slides
7 Applied Examples: Various

Additional Resources

My introduction to causal inference in general was through Cyrus Samii's PhD level course in 2012, for which the updated version of slides and other resources are available here. Bruce Hansen kindly makes his regularly updated Econometrics text available online free here. Athey and Imbens taught a course on machine learning and econometrics for which materials are available here (scroll down).

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