Causal Inference in R: Introduction

> who_are_we(c("lucy", "malcolm", "travis"))

        https://www.lucymcgowan.com/

    https://www.malco.io/

https://travisgerke.com/

The three practices of analysis

1. Describe
2. Predict
3. Explain

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.

For causal inference, we need to make sometimes unverifiable assumptions.

Today, we’ll focus on the assumption of no confounding.

Tools for causal inference

• Causal diagrams
• Propensity score weighting
• Propensity score matching
• G-methods & friends

Other tools for causal inference

• Randomized trials
• Instrumental variables & friends
• TMLE and other ML for causal inference

Resources

Causal Inference in R: Our book! Free online.

Causal Inference: Comprehensive text on causal inference. Free online.

The Book of Why: Detailed, friendly intro to DAGs and causal inference.

Mastering ’Metrics: Friendly introduction to IV-based methods