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boosting (1)

causal forest (1)

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decision trees (1)

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heterogeneous effects (1)

image analysis (1)

image embeddings (1)

image processing (1)

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machine learning (9)

natural language processing (1)

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political economy (1)

potential outcomes (1)

prediction (2)

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ridge (1)

sentiment analysis (2)

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text extraction (1)

treatment effects (5)

unconfoundedness (1)

variable selection (2)

word embeddings (2)

All Modules

This section contains all materials and case studies to support a variety of classes. Filter by topic using the categories on the right. Filter by class by choosing the appropriate class category.

Modules

A Study of Richter’s Kouroi Through Image Embedding

Using examples from Richter’s Kouroi, this notebook introduces computer vision and image embeddings, showing how pre-trained convolutional neural networks can be applied…

A visual introduction to fundamental modern machine Learning

What is Deep Learning, Machine Learning, and Artificial Intelligence?

Double Machine Learning

The seventh notebook in the Causal ML stream. We combine everything: the partially linear model, the residual-on-residual idea from Notebook 4, and any learner we like for…

Embedding Driven Text Analysis of Crease’s Stance Towards Chinese Immigrants

This notebook aims to demonstrate how machine learning can assist with historical and other humanities research.

From Prediction to Treatment Effects

The fourth notebook in the Causal ML stream. We move away from prediction to causality. We meet potential outcomes, the average treatment effect, selection bias, and the…

Heterogeneous Effects and Causal Forests

The eighth and final notebook in the Causal ML stream. We move from the average effect to who benefits. We meet the conditional average treatment effect, estimate it with…

How Do Large Language Models Predict?

This notebook explores how large language models (LLMs) work by linking their probability-based predictions to real-world applications. We start with the basics of next-word…

Image Analysis Workshop: Computer Vision for Social Science Research

This workshop notebook introduces practical image analysis for social science research, including manual preprocessing, similarity and clustering (BoVW), deep learning…

Introduction to Convolutional Neural Networks (CNNs)

This notebook provides an introduction to Convolutional Neural Networks (CNNs), their architecture, and their applications. It includes a hands-on example of building and…

Introduction to Convolutions

This notebook provides an intuitive explanation of convolution and its application in multiple fields with some simple examples.

Introduction to Network Analysis

This notebook is an introduction to basic network analysis in Python.

Introduction to Sentiment Analysis: Identifying and Mapping Disinformation Campaigns using NLP

An introduction to BERT-based NLP using online examples of disinformation and exploration of how embeddings and classification models can be used to analyze and detect…

Network Analysis: CBDB Dataset

Using Network Analysis to Analyze The China Biographical Dataset

Network Analysis: KINMATRIX Dataset

Using the KINMATRIX Dataset to explore network analysis for SOCI 415

Optical Character Recognition (OCR)

Post-Double-Selection Lasso

The fifth notebook in the Causal ML stream. We return to the growth data from Notebook 2 and the convergence question. A naive regression shows no convergence, and so does a…

Prediction, Inference, and Causality

The first notebook in the Causal ML stream. We meet the supervised-learning way of thinking: prediction versus inference, train/test splits, loss functions…

Propensity Scores and Doubly-Robust Estimation

The sixth notebook in the Causal ML stream. We return to the 401(k) question and adjust for confounders a second way: by modelling who gets treated. We estimate the…

Regularization: Ridge and Lasso

The second notebook in the Causal ML stream. We encounter the ‘too many controls’ problem, where ordinary least squares falls apart once the number of covariates approaches…

Text Analysis Overview

Qualitative research often begins with hand-coded analysis, where researchers create a codebook and apply categories to text manually

Trees, Forests, and Boosting

The third notebook in the Causal ML stream. We show a different way to be flexible: decision trees, which split the data into boxes and predict the local average. A single…

Word Embeddings and National Constitutions

Using word embeddings to compare the language of autocratic and democratic national constitutions.