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2011 HSR&D National Meeting Abstract

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2011 National Meeting

2008 — Modern Approach to Causal Inference

Sauer B (SLC IDEAS Center), Greene T (SLC IDEAS Center), Redd A (SLC IDEAS Center)

Workshop Objectives:
Introduce the audience to modern theories and methods for causal inference: A. Define the counterfactual framework for causal effects; B. Present assumptions required for causal inference in randomized and non-randomized studies; C. Present the use of directed acyclic graphs and background knowledge to select a minimal set of covariates to produce conditional exchangeability required for observational studies; D. Describe the use of statistics developed specifically to address the counterfactual missing data problem.

1. Present probability notation for counterfactual framework and rationale for studying probabilistic causal effects; 2. Define and discuss the crucial difference between causal and associational effect estimates; 3. Discuss how ideal randomized trials can be used to estimate counterfactual causal effects; 4. Describe the assumptions required to estimate average causal effects from observational data; 5. Describe the use of Directed Acyclic Graphs to define variable structural types and identify the minimal subset of variables required for conditional exchangeability; 6. Present the unified therapy of bias based on causal structures; 7. Present statistical techniques specifically developed to estimate counterfactual causal effects; 8. Use simulated example that has both counterfactual and observed data to demonstrate covariate selection issues and statistical modeling; 9. Share the R code with attendees that we used to simulate data for pedagogical purposes.

Target Audience:
Clinical researchers, methodologists, statisticians.

Assumed Audience Familiarity with Topic:
The audience should have a basic understanding of randomized controlled trials, observational research, and regression modeling.

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