Causal Inference Using Survey Data

About

This course will introduce you to the concepts and methods of causal inference and causal modeling in the social sciences. It will highlight the relevance of research design, analytical methods, and their systematic combination to optimize the validity of causal inferences drawn from empirical studies. You will learn the key principles and techniques of causal inference, including potential outcomes, counterfactuals, and causal graphs, and will get to know the experimental approach to causality. Building on existing knowledge concerning linear regression modelling and research design, the course will then cover key methods of causal modeling using survey data, such as fixed effects panel models, matching, difference-in-differences, regression discontinuity, and instrumental variables. Throughout the course, you will apply these concepts and methods in hands-on sessions to real-world examples in the social sciences. The application will be conducted with the statistical software package Stata. A solid background in Stata is expected. By the end of the course, you will have the skills and knowledge to design, conduct, and interpret causal inference studies in the social sciences. You will be able to engage with the contemporary literature of causal inference and identify state-of-the-art methods which might be most relevant to your specific research question.

Course leader

Heinz Leitgöb is Interim Professor of Quantitative Research Methods at the Institute of Sociology, Leipzig University. Tobias Wolbring is Professor of Empirical Economic Sociology at School of Business, Economics, and Society at FAU Erlangen-Nürnberg.

Target group

You will find the course useful if you:
- have a background in the social, behavioral or economic sciences (economists, political scientists, sociologists, criminologists, psychologists, etc.),
- are interested in methods for causal inference based on experimental and/or observational data, especially panel data,
- have a firm knowledge in linear regression modelling,
- are motivated to apply the concepts and statistical approaches in hands-on sessions.

Prerequisites
- Knowledge of basic statistical concepts and their formal background, including the principles of linear and binary logistic regression
- Solid background in stata
- Basic understanding of designing quantitative studies

Course aim

By the end of the course, you will:
- have a good understanding of the potential outcome framework, causal diagrams, and the counterfactual way of thinking,
- be capable of designing your own study to derive causal estimates in observational settings,
- acquire an in-depth understanding of and the skills to apply five families of methods: fixed effects models, matching, difference-in-differences, instrumental variables, and regression discontinuity design,
- become familiar with interdisciplinary applications of the methods covered by the course,
- be able to engage the contemporary literature of causal inference and identify state-of-the-art methods which might be most relevant to your specific research question.

Fee info