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LinkedIn Learning

Causal Inference with Survey Data

via LinkedIn Learning

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Overview

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Explore the concepts of causal inference in survey data, learn some of the underlying theory of causality, and focus on empirical methods to identify causality in data.

Syllabus

Introduction
  • Causality unlocked: A primer for data analysts
  • What you can learn
  • What you should know
1. Cause and Effect
  • Why causal inference matters
  • The gold standard: Experimental data
  • What is different about survey data?
  • Observables vs. unobservables causes
  • What are treatment effects?
  • An applied example: The LaLonde debate
2. Experimental Survey Designs
  • Setting up a randomized controlled trial
  • Analyzing a randomized controlled trial
3. Cross-Sectional Survey Designs
  • Surveys with cross-sectional data
  • Regression analysis
  • Propensity score matching
  • Regression discontinuity designs
  • Instrumental variable models
4. Longitudinal Survey Designs
  • Surveys with longitudinal data
  • Regression models with time effects
  • Fixed effects regression models
  • Difference-in-difference estimation
  • Synthetic control methods
5. Other Models
  • How to evaluate causal robustness
  • How to present causal statistics
Conclusion
  • Next steps and additional resources

Taught by

Franz Buscha

Reviews

4.5 rating at LinkedIn Learning based on 12 ratings

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