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.

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