Resources

The best complement to the workshop is our book, so make sure to check it out!

Further reading

Other books

There are also several other excellent books on causal inference. Our book is different in its focus on R, but it’s still helpful to see this area from other perspectives. A few books you might like:

• Causal Inference: What If?
• Causal Inference: The Mixtape
• The Effect

The first book is focused on epidemiology. The latter two are focused on econometrics. We also recommend The Book of Why for more on causal diagrams.

Interesting papers

Here are some interesting papers we commonly mention in the workshop or related to key topics:

• A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks: A musing on the tasks of data science (description, prediction, and causal inference)
• To Explain or Predict: A detailed analysis of the differences between causal and predictive modeling.
• Choosing the Causal Estimand for Propensity Score Analysis of Observational Studies: A discussion of when to use different estimands. Includes a helpful table with a summary.
• To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias: A simulation study showing that for m-bias, confounding is usually a bigger issue than collider bias.
  
• Effects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates: A simulation study that shows, for many analyses, the risk of confounding is greater than the risk of bias from adjusting for an instrumental variable.
• tipr: An R package for sensitivity analyses for unmeasured confounders: A detailed introduction to tipr.
• Metformin use and incidence cancer risk: evidence for a selective protective effect against liver cancer: The paper we reference in the sensitivity analysis section.