Adjustment-Based Methods
22
G-Methods 1: Inverse Probability Weighting
Causal Inference for Intervention & Service Evaluations: A Practical Handbook
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Welcome
Introduction
1
Background & Motivation for Causal Inference in Service Evaluations
2
A Brief History on Conceptualising Causality
3
Common Assumptions in Causal Inference Methodology
4
Existing Guidance on Study Design Selection
5
Unifying Guidance on Choosing Causal Inference Methods (Decision Tree Diagram)
6
Key Resources
7
Coverage of Causal Inference Methods in Key Texts
Key Concepts in Causal Inference
8
Randomized Controlled Trials
9
Directed Acyclic Graphs
10
Target Trial Emulation
11
Defining Treatment Strategies
Quasi-Experimental Methods
12
Difference-in-Differences
13
Interrupted Time Series
14
Synthetic Controls
15
Regression Discontinuity
16
Instrumental Variables
17
Extensions of Quasi-Experimental Designs
Adjustment-Based Methods
18
Outcome Regression (Selection on Observables)
19
Matching (Exact & Covariate)
20
Propensity Scores
21
G-Methods: An Introduction
22
G-Methods 1: Inverse Probability Weighting
23
G-Methods 2: Parametric G-Formula
24
Extensions of Adjustment-Based Methods
Applying Causal Methods: Worked Examples in the NHS
25
Case Study 1a: DOAC Head-to-Head Emulated Trial
26
Case Study 1b: DOAC Policy Prescription Change Interrupted Time Series Analyses
27
Case Study 2: Ophthalmology Hub of Care Model Interrupted Time Series Analyses
28
Practical Barriers to Implementation: Common Challenges in Causal Inference
29
Conclusion
30
About the Author & Acknowledgements
References
Adjustment-Based Methods
22
G-Methods 1: Inverse Probability Weighting
22
G-Methods 1: Inverse Probability Weighting
Coming Soon
21
G-Methods: An Introduction
23
G-Methods 2: Parametric G-Formula