Causal Inference with AI and Machine Learning

Covers the foundations of causal inference and how modern machine learning methods can be used to estimate causal effects from observational and experimental data. Topics include instrumental variables, difference-in-differences, regression discontinuity, and double machine learning.

Causal Inference and Advanced Quantitative Methods

An advanced doctoral seminar on research design and causal identification, covering potential outcomes, matching, synthetic control, and Bayesian approaches to causal inference in the social sciences.

Machine Learning and AI for Public Administration and Policy

Introduces supervised and unsupervised machine learning with a focus on policy-relevant applications, including algorithmic fairness, automated decision-making in government, and the ethical use of AI in public sector contexts.

Modern Text Analysis and Machine Learning for Social Research

A doctoral course on computational text analysis, covering topic models, word embeddings, transformer-based language models, and their applications to large-scale social science research.

Applied Machine Learning

A hands-on introduction to machine learning for policy professionals, covering prediction, classification, and model evaluation with applications to public management and policy analysis.

Introduction to Quantitative Methods and Regression Analysis

Foundations of applied statistics and regression for public policy, including probability, hypothesis testing, ordinary least squares, and interpretation of results for non-technical audiences.

Introduction to Research Methods

An overview of research design for public administration students, covering qualitative and quantitative approaches, data collection, measurement, and the logic of inference.

Politics and Technology

An undergraduate survey of the relationship between technological change and political life, examining social media, surveillance, algorithmic governance, and the politics of AI.