Causal Inference in R: Introduction
2020-07-29 (updated: 2020-07-28)
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The three practices of analysis
- Describe
- Predict
- Explain
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Normal regression estimates associations. But we want counterfactual, causal estimates:
What would happen if everyone in the study were exposed to x vs if no one was exposed.
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For causal inference, we need to make sometimes unverifiable assumptions.
Today, we'll focus on the assumption of no confounding.
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Tools for causal inference
- Causal diagrams
- Propensity score weighting
- Propensity score matching
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Other tools for causal inference
- Randomized trials
- G-methods & friends
- Instrumental variables & friends
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Let's head to RStudio Cloud: https://bit.ly/causalcloud
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Resources
Causal Inference: Comprehensive text on causal inference. Free online.
The Book of Why: Detailed, friendly intro to DAGs and causal inference. Free online.
Mastering 'Metrics: Friendly introduction to IV-based methods
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