This module will provide you with the tools to conduct sound data analysis based on careful combination of theory and statistical methods to arrive at supportable conclusions. The course will first cover finding and adapting an appropriate existing conceptual framework based on theories of health production, and generating from this a meaningful, falsifiable research hypothesis. It will then explain core concepts in building directed acyclical graphs (DAGs) to represent the causal processes reflected in research hypotheses, and converting these DAGs into a statistical analysis plan. Finally, it will cover practical statistical analysis to test hypotheses using both standard regression and marginal structural model approaches. The core of the module will be student practical implementation of methods presented through lectures, with examples drawn from a wide range of topics but with a focus on infectious diseases. After taking this module you should be able to: Describe key theories of how health and illness are patterned across populations.Outline the principles behind, and key features of, directed acyclical graphs (DAGs).Construct a causal DAG that reflects assumptions about how factors relate to one-another, including confounding and selection bias.Explain the difference between standardization and stratification for managing confounding and bias.Use an appropriate theory and conceptual framework to build a DAG for a testable hypothesis about health outcomes.Build an approach to address confounding or bias in design or analysis of data.Discuss how theories and DAGs for infectious disease may differ from, and be similar to, those for non-communicable health outcomes.Implement an analysis using marginal structural models in a statistical package.Compare and contrast results from regression and marginal structural models.Discuss the strengths and weaknesses of various quasi-experimental methods for finding causal effects. The module is open to students on the MSc/PG Dip Global Health and Development, other UCL MSc/PG Dip students, TropEd students, Taster course students and Short course students. Prerequisites for this module include experience working in Stata and an understanding of different study designs and linear/logistic regression methods. Some understanding of theories of health would be helpful. You will have a mix of teaching, supervised computer lab sessions and self-directed reading and learning. Interactive lectures will include group discussion based on lectures and readings, and a mid-module class presentation of final project ideas for peer review. The module will also prioritize time for private reading of materials and group and self-study for the final project – while the assessment will be individual the implementation of standard methods will provide opportunity for group-work. Moodle will be used to give students access to reading materials.   The module will be assessed by a 2000-word individual essay (100%) conducting a quantitative analysis of a research question justified with reference to the literature. This will be based on a dataset provided to you. There will be a brief formative classroom presentation midway through the course to provide feedback on your planned topic.