- POLSCI 748 Introduction to Causal Inference

Theory and practice of causal inference in the social sciences, introduces basic concepts, such as counterfactuals and identification. Introduces the formal frameworks of potential outcomes and graphical models (DAGs). Covers experiments (in the lab and the field), and various regression-based approaches. - POLSCI 146 Politics and Economics

Politics is about choices that affect the distribution of gains and losses, and about societal and political conflicts surrounding them. Course analyzes how political and economic forces shape: (1) Historical origins, such as the industrial revolution, slavery, and the birth of the modern welfare state; (2) Macro-economic policies, such as the taxation of capital, public spending and debt; and (3) Redistributive policies, such as welfare programs, unemployment and health insurance, and the minimum wage. - POLSCI 733 Advanced Regression

Theory and practice of likelihood inference for social science models, spanning binary, nominal, ordinal, count, and continuous random variables. Estimation, interpretation, and presentation of results will also be emphasized. - Advanced Bayesian Models

This second part of the Bayesian workshop series introduces more advanced models. We will cover Bayesian versions of latent variable models, such as factor models and item response theory or ideal point models, as well as models for simultaneous outcomes, such as seemingly unrelated regression and multivariate probit models. Furthermore, we will discuss models to deal with the ubiquitous problem of missing data in a fully Bayesian context. Each lecture is followed by lab sessions, where we replicate examples from the lecture and discuss how to understand fitted models through predictions and graphical displays.. - Regression designs and their applications

Does incumbent performance affect chances of re-election? Do get-out-the-vote election campaigns actually increase turnout? Do citizen information programs increase political participation? And if so, by how much? These and related questions in political science might seem straightforward, but answering them properly is harder than you might think. In this course we will encounter a framework for thinking about questions like these from the perspective of causal inference. This means thinking about how we can best estimate the effects of causes (such as peace keeping missions) on outcomes. In a perfect world we could conduct experiments, but often we are left with using limited and noisy pre-existing data. We will discuss a number of regression models, which will provide us with a toolbox for tackling these kinds of causal questions. The course will consist of both theoretical and practical sessions. - Quantitative Methods in Political Science

This course introduces graduate students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to likelihood as a theory of inference, including models for binary and count data. Statistical language used is R. - Models for Categorical Data

This course introduces advanced undergraduate students to models for categorical data. Its main focus lies on discrete choice models and their application. The use of simulation methods in emphasized, in order to calculate quantities of interest that can be easily communicated. Practical lab session using R and Zelig focus on presentation and interpretation of results. - Introduction to Missing Data

This workshop introduces the problem of missing data and modern solutions for it. I discuss different types of missing data and still predominant (but deficient) strategies for dealing with them. I show that multiple imputation is a solid method to deal with a wide range of missing data that is easily carried out using Stata and R. Besides the classical multivariate normal imputation methods, I demonstrate how chained regression imputation can be used to impute genuinely categorical data. - Modeling heterogeneity in cross-sectional and panel data

Essex Summer School

The course introduces a range of recently developed methods that deal with heterogeneity. Heterogeneity refers to differences between groups, such as individuals and countries, which are due to omitted or unobserved factors. Commonly employed approaches try to correct for such factors. We will take a different approach and build explicit models for heterogeneity between individuals or groups (such as countries or organizations) allowing us to answer questions such as: Does the effect of income on redistribution preferences differ between different subgroups of individuals? Does the effect of parties’ policy on vote choice differ between countries? Do different subgroups of a population follow different patterns of attitude or preference change? Do unemployment and poverty dynamics differ between (unobserved) subgroups? - Advanced Bayesian Models for the Social Sciences

ICPSR Summer Programm

The course covers the theoretical and applied foundations of Bayesian statistical analysis at a level that goes beyond the introductory course. Therefore, knowledge of basic Bayesian statistics (such as that obtained from the 'Introduction to Applied Bayesian Modeling for the Social Sciences' workshop) is assumed. The course will consist of four modules. First, we will discuss Bayesian stochastic simulation (Markov chain Monte Carlo) in depth with an orientation towards deriving important properties of the Gibbs sampler and the Metropolis Hastings algorithm. Extensions and hybrids will be discussed. Second, the course will cover model checking, model assessment, and model comparison, with an emphasis on computational approaches. The third module introduces Bayesian variants of "workhorse" political science models, such as linear models, models for binary and count outcomes, discrete choice models, and seemingly unrelated regression. The fourth week will focus on more advanced Bayesian models, such as hierarchical/multilevel models, models for panel and time-series cross-section data, latent factor and item response theory (IRT) models, as well as instrumental variable models. Throughout the workshop, we emphasize not only estimation with modern programming software (R, and JAGS), but also how to communicate results effectively.

## Teaching

Duke UniversityUniversity of Mannheim