Causal Inference in R: Introduction

2020-07-29

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




        https://www.lucymcgowan.com/




               https://www.malco.io/

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

  1. Causal diagrams
  2. Propensity score weighting
  3. Propensity score matching

Other tools for causal inference

  1. Randomized trials
  2. G-methods & friends
  3. Instrumental variables & friends

RStudio Cloud: https://bit.ly/causal-r-cloud

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