MY580: Design-based causal inference in observational and experimental settings
Date:
26/10/2026
Organised by:
London School of Economics and Political Science
Presenter:
Dr Zach Dickson
Level:
Intermediate (some prior knowledge)
Contact:
Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.
Design-based causal inference in observational and experimental settings by Dr Zach Dickson - LSE
This course will introduce students to the principles and methods of causal inference. Causal inference is the process of drawing conclusions about the causal relationships between variables based on observational data. In this course, we will start with an overview of the potential outcomes framework and will cover topics such as confounding, selection bias, and causal identification. We will also discuss directed acyclic graphs (DAGs), and methods for estimating causal effects in observational settings, including difference-in-differences, instrumental variables, and regression discontinuity designs. We will use real-world examples to illustrate the concepts and methods covered in the course, and students will have the opportunity to apply these methods to their own research projects.
By the end of the course, students will have a solid understanding of the principles and methods of causal inference, and will be able to apply these methods to a wide range of research questions. A basic knowledge of programming in a scripting language, such as R or Python, is recommended for this course.
Cost:
Free
Website and registration:
Region:
Greater London
Keywords:
Data Quality and Data Management , Quantitative Data Handling and Data Analysis, Causal Inference
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Data Quality and Data Management
Quantitative Data Handling and Data Analysis