A Cartoon Guide to Causal Inference
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This document is a cartoon guide to causal inference by Ellie Murray. It discusses how causal inference aims to estimate what would happen if aspects of the world were different, such as through randomized experiments or statistical methods. It notes that intention-to-treat effects from randomized trials require no assumptions, while per-protocol effects require assumptions like no unmeasured confounding and positivity. Well-defined interventions are also important for consistency. The goal of causal inference is to understand what would happen under counterfactual scenarios, like if we could travel back in time.
A Cartoon Guide to Causal Inference
- 1. @EpiEllie A cartoon guide to causal inference Ellie Murray Department of Epidemiology www.scholar.harvard.edu/eleanorm urray B W H F o r u m f o r A d v a n c e d B i o m e d i c a l C o m p u t a t i o n 2 0 1 9
- 2. What do we want to know? How would things have changed if the world had been slightly different? @EpiEllie – A cartoon guide to causal inference
- 3. What do we want to know? If we can’t have a time machine, we’d like to have a randomized trial. If we can’t have a randomized trial, we’d like to emulate what would have happened if we could have done one. @EpiEllie – A cartoon guide to causal inference
- 4. What effects should we estimate? Option 1 Intention-to-treat effects: How does the outcome change if everyone is randomized to exposure A vs exposure B? @EpiEllie – A cartoon guide to causal inference
- 5. What effects should we estimate? Option 2 Per-protocol effects: How does the outcome change if everyone is receives exposure A vs exposure B? @EpiEllie – A cartoon guide to causal inference
- 6. One way to choose: patient- centered causal effects Murray EJ, et al. Journal of Clinical Epidemiology. 2018; @EpiEllie – A cartoon guide to causal inference
- 7. One way to choose: patient- centered causal effects Murray EJ, et al. Journal of Clinical Epidemiology. 2018; @EpiEllie – A cartoon guide to causal inference
- 8. But we can’t just use the data In randomized trials or in observational studies: if we want to estimate the per- protocol effect (i.e. the treatment effect), we need to make assumptions @EpiEllie – A cartoon guide to causal inference
- 9. What assumptions do we need? 1. No unmeasured confounding: all common causes of the treatment and outcome are known and measured in the data @EpiEllie – A cartoon guide to causal inference
- 10. Intention-to-treat effect has no unmeasured confounding @EpiEllie – A cartoon guide to causal inference
- 11. Per-protocol effect requires adjustment for confounding @EpiEllie – A cartoon guide to causal inference
- 12. What assumptions do we need? 2. Positivity: non-zero probability of all levels of treatment for all individuals in our target population (i.e. variability in exposure) @EpiEllie – A cartoon guide to causal inference
- 13. Positivity can be a problem for per-protocol effects in: Randomized controlled trials Observation al studies @EpiEllie – A cartoon guide to causal inference &
- 14. What assumptions do we need? @EpiEllie – A cartoon guide to causal inference 3. Consistency: our treatment levels are clear and well-defined Are we asking a specific enough question to get an answer we can understand?
- 15. Why are well-defined interventions important? When there are multiple possible ‘interventions’ and we don’t specify one, our answer is a weighted average of all interventions but we don’t know the weights @EpiEllie – A cartoon guide to causal inference
- 16. Why are well-defined interventions important? But, if the intervention is ill-defined the confounding is probably also ill- defined! @EpiEllie – A cartoon guide to causal inference
- 17. Summary Causal inference is about what would have happened if the world had been just a little different. If we had a time machine, we could know. Instead, we experiment. If we can’t even experiment, we use statistics & assumptions to estimate what would have happened in a world where we could@EpiEllie – A cartoon guide to causal inference