Casual Interference for Observational Data

Description:

Casual Interference for Observational Data
Dr Zach Dickson

This course will introduce students to the principles and methods of causal inference for observational data. 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 methods for estimating causal effects in observational settings, including difference-indifferences, 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 for observational data, 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.

Session Details
Time: 10:00 - 15:00 (12:00 - 13:00 Lunch break)
Date: 26 February 2025
Mode: Hybrid - In person at CON 1.01 and on Zoom
*Zoom link will be circulated to participants when sign-up closes

Cost:

Free

Website and registration:

Region:

Greater London

Keywords:

Frameworks for Research and Research Designs, Data Collection, Data Quality and Data Management , Quantitative Data Handling and Data Analysis, ICT and Software, dtp, esrc, doctoral training, methodology, methods, method reseach

Related publications and presentations from our eprints archive:

Frameworks for Research and Research Designs
Data Collection
Data Quality and Data Management
Quantitative Data Handling and Data Analysis
ICT and Software

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