Causal Inference, Surveys and Missing Data for Population Health
Modern statistical methods for incomplete data: complex survey design and analysis, missing data techniques, and causal inference for population health.
Summary
Lectures are small, with a median of 11 enrolled students in the recorded sections.
What you’ll be able to do
Identify the design elements (stratification, clustering) and sampling weights (unequal probability sampling, non-response adjustments) of a complex survey design and perform regression analyses of complex survey data to answer epidemiologic and public health research questions using standard statistical software (SAS and R)
Identify missing data mechanisms and their potential impacts on naive statistical analyses
Explain the strengths and weaknesses of various strategies for dealing with missing data (weighting, maximum likelihood, Bayesian, multiple imputation) and implement them using standard statistical software (SAS and R)
Define individual and average causal effects and identify challenges (confounding, selection bias) to their estimation in observational studies
Identify and implement statistical methods for estimating causal effects (IP weighting, g-formulas, propensity scores, instrumental variables) in settings where their use is appropriate
Source: UW Guide 1· UW Guide 2
Grade history
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Prerequisites
Course mapB M I/POP HLTH 552,STAT/F&W ECOL 572(or HORT 572 prior to Spring 2025),C&E SOC/SOC 361, or ED PSYCH 761
This is a best-effort interpretation; check the catalog requirements above.
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Professors
Spring 2027No instructors recorded for this selection.
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Student experience
the class
Overview of modern statistical methods for dealing with "incomplete" data, including the design and analysis of complex surveys, the analysis of missing data, and causal inference.
difficulty & workload
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Topics
- Complex survey design and analysis
- Missing data analysis
- Causal inference
Skills
- Complex survey design analysis and regression in SAS and R
- Missing data strategy implementation including weighting, ML, Bayesian, and multiple imputation
- Causal effect estimation using IP weighting, g-formulas, propensity scores, and instrumental variables
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Explore recorded gradesRecorded 2026-10-14
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Model & dataset provenance
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