SGIA40F15: Causal Inference

• None.

• None.

• None.

• To provide an overview of causal inference frameworks and of research designs and techniques used in social science and other fields to answer questions on causation.
• To facilitate understanding of different causal inference designs and techniques.
• To teach the application of causal inference designs and techniques using open source software tools.
• To introduce state of the art applications of causal inference techniques to research topics from the domain of social science.

• The material is organized into five lecture-seminar pairs. Each pair first provides a theoretical and conceptual overview of a set of causal inference tools and then practices them on simulated and real examples from the domain of social science.
• The five pairs may include topics such as; Randomized experiments, Selection on observables: matching, and regression, Difference-in-Differences, Synthetic Control, Matrix Completion, Instrumental Variables, Regression Discontinuity Design, Double Machine Learning.

Subject-specific Knowledge:

• By the end of the module students will have a working knowledge and understanding of the following areas:
• Frameworks for causal inference and the specialised use of causal inference techniques in social science research;
• Computational implementation of advanced causal inference techniques;
• Complexity of strengths and limitations of different causal inference designs and techniques in social science.

Subject-specific Skills:

• Navigate the landscape of causal inference research design
• Demonstrate a working technical knowledge of how to use causal inference techniques;
• Ability to interpret and communicate causal analyses to different audiences.

Key Skills:

• Students will also develop some important key skills, suitable for underpinning study at this and subsequent levels, such as:
• Critical appraisal of advanced causal inference designs and techniques and of their applications.
• Application of experimental and observational designs for causal inference.
• Application of causal inference techniques, including in complex and specialised contexts.
• Interpret and communicate causal inferences to wider audiences.

• Lectures will demonstrate the conceptual foundations of causal inference in social science settings.
• Seminars: enable students to practice some of the key causal inference techniques discussed in the module, using their implementations in R.
• Independent Reading: provides students with the opportunities to read widely, particularly in preparation for formative and summative assessments. Independent reading enables students to engage in debates within scholarly journals and research monographs, in ways that enhance a critical understanding and engagement with key issues in causal inference.
• Summative assignment is designed to test the acquisition and articulation of knowledge and critical understanding, and skills of implementation and interpretation of causal inference techniques as applied to real social science problems, by conducting and reporting causal analyses. Students will have the option to complete and submit up to 5 causal analysis reports, the average mark of the best 3 will form the modules mark. If less than three reports are submitted, missing reports will be given a mark of zero.

If you have a question about Durham's modular degree programmes, please visit our Help page. If you have a question about modular programmes that is not covered by the Help page, or a query about the on-line Postgraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.