This is Maria's slide set from the 2016 SER debate with Miguel Hernán re whether variables can be causes if they do not correspond with interventions. Video at: https://epiresearch.org/causal-parameters-without-corresponding-experiments/

1. Can a variable be a cause if it
doesn’t correspond with an
intervention?
Can we disagree about philosophy but agree about epidemiology?
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2. Session motivated by the “No Manipulation-
No Causation” debate
• “Effects” are the difference in potential outcomes under two alternative
exposures or intervention strategies
• Therefore, impossible to define an effect of something that cannot be intervened
upon
• Easy question: where will I wake up if I take the blue pill instead of the red pill?
• Hard question: would I go fishing more often if I were a man?

3. Session motivated by the “No Manipulation-
No Causation” debate
• “Effects” are the difference in potential outcomes under two alternative
exposures or intervention strategies
• Therefore, impossible to define an effect of something that cannot be intervened
upon
• Easy question: where will I wake up if I take the blue pill instead of the red pill?
• Hard question: would I go fishing more often if I were a man?
3

4. Session motivated by the “No Manipulation-
No Causation” debate
• “Effects” are the difference in potential outcomes under two alternative
exposures or intervention strategies
• Therefore, impossible to define an effect of something that cannot be intervened
upon
• Easy question: where will I wake up if I take the blue pill instead of the red pill?
• Hard question: would I go fishing more often if I were a man? A black man?
4

5. NO Manipulation-NO Causation Perspectives
•Strict NOMNOC (Holland- the father of NOMNOC):
• “Causes are experiences that units undergo and not attributes
that they possess”
• Ruled out age, sex, or race as causes
•Flexible NOMNOC: okay to think of
implausible/impossible interventions
• As long as the intervention does not “fundamentally” change the person.
• “actions” are causes, “states” are not causes: exercise regimen is a
coherent cause, BMI is not (Taubman and Hernan)
• NOT NOMNOC: Manipulation is not necessary, or if you
permit implausible interventions, not much is ruled out
• Sex is randomized at conception. It’s easy to assess the
effects.
• Ignore NOMNOC criterion: Who cares? let’s just talk
about the “mediators”.
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6. Social epidemiologists love
to argue about NOMNOC
•Race doesn’t affect your health- racism affects health!
•Race doesn’t affect your health- changing your race
would make you a whole different person.
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7. Social epidemiologists love
to argue about NOMNOC
•Race doesn’t affect your health- racism affects health!
•Treating race as a cause does not reify race, or deny the
obvious facts that “race” is fluidly defined within social
contexts and social context shapes the effects of race on
health.
•Treating race as a cause does not privilege genetic or
social mechanisms for racial inequalities. It merely opens
these up for rigorous evaluation.
•Race doesn’t affect your health- changing your race
would make you a whole different person.
•What defines me a person? My eye color? My hair color?
My parents’ SES? This is an impossible rabbit hole.
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8. Why the NOMNOC perspective is (often) useful
• Specifying an intervention that corresponds with the exposure
variable clarifies what you mean and helps guide public health
actions
• Income = Wages? Lottery winnings? Welfare?
• Physical activity = Marathons? Boxing? Wheelchair stretches?
• This is a huge advantage, and it has more practical, day to day implications for
how we do research than the disadvantages
• Although in theory either mistake -- to fail to articulate the corresponding
intervention OR to ignore important non-manipulable risk factors – is
relevant…
• ….in practice, failing to articulate the corresponding intervention is more
common and more pernicious.
• MG’s perspective: People who deny race/sex are causes usually analyze data
as if they believed race/sex were causes
• (VanderWeele and Robinson 2014 provided a coherent way for them to talk about their regressions)8

9. Why the NOMNOC perspective is damaging
• Using causal language allows access to an array of useful
machinery for causal inference for many important questions:
• DAGs, d-separation rules, recognition of collider bias
• How do you select control variables? How do you interpret adjusted regression
coefficients?
• Do you control for income when estimating the effect of sex on depression?
• NOMNOC handicaps researchers interested in racial and gender
disparities, because it is unclear how to adopt and use causal
inference framework.
• If we can’t talk about race and sex as causes, discussing
inequalities and the mechanisms by which race and sex
influence health is a tongue twister and fails to communicate
• The effect of race on CVD is mediated by…
Becomes
• The non-causal correlation between race and CVD is attenuated by
adjusting for… 9

10. Why the NOMNOC perspective is damaging
• If you cannot conceptualize “states” as causes, you cannot
generalize from one intervention to variations on that
intervention that would have the same effects
• Reducing blood pressure with drug A reduces MI risk. Maybe other drugs that
reduce blood pressure would also reduce MI risk.
• It is knowledge of the mediating state between an intervention and an outcome
that guides development of new interventions
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11. Miguel perspective [slides deleted]
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12. What we disagree on
• MG: Strict NOMNOC is a handicap. Flexible NOMNOC is largely harmless.
• MG: You can apply d-separation, causal inference machinery and thinking while
treating race, sex, and states as causes.
• MG: refusing to define sex or race as a cause makes it difficult to take advantage
of the tools for rigorously evaluating the origins of inequalities
• MH: Ok, let’s say race is a cause. How does that help us interpret the number
we obtain from data when comparing health outcomes in, say, blacks and
whites? How does it help us decide what to do next? Whatever you answered to
the last question, wouldn’t you do the same next thing after observing the
white/black differences even if you didn’t say “race is a cause”?
• Is it useful to conceptualize “states” rather than “actions” as causal?
• MG we learn generalizable information by defining states as causes and investigating
• MH: Wait, wouldn’t we equally investigate after finding differences between different
states even if we don’t say “this state is a cause”?
• MG: Sure, you don’t have to say it, but wouldn’t it be easier to use words to describe
this idea?
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13. What we agree on
• It is usually helpful to articulate an intervention that would correspond
with the exposure of interest
• Exceptions: descriptions of inequality when the “cause” is not of interest as a
target of intervention (e.g., race or sex) but rather the mechanisms
• Interventions do not need to be humanly feasible
• Interventions on certain variables are ambiguous and usually entail
more description to be clear: if we intervene on “race” do we also
intervene on location of birth, to match the geographic distribution of
births of the other race?
• Many “state” variables correspond with many different possible
interventions on distinct underlying variables, which may all have
different consequences when wiggled.
• Racial and gender inequalities in health are deplorable, modifiable, and
we should seek interventions to eliminate such inequalities.
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14. Maria’s response
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15. The argument against states as causes:
Distinguishing “fat hand” problems from
consistency problems
• Fat hand interventions are interventions that
influence more than just the specific exposure
of interest
• The intervening hand was too fat to just grab
the precise exposure of interest
• As in pickup sticks, the fat hand touches lots of
sticks and has many extraneous consequences
• For some exposures, all known interventions
may be “fat hand”
• BMI: diet, exercise, surgery…
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16. Distinguishing “fat hand” problems from
consistency problems
• Fat hand interventions influence more than
just the specific exposure of interest
• Consistency violations reflect a measurement
that is too crude: exposures with different
outcomes are called the same thing, ie the
exposure is heterogeneous
• BMI: subcutaneous adipose tissue, visceral
adipose tissue
Consistency violations make it difficult to predict the consequences of future
interventions, because you do not know which flavor of the exposure has
which effect. 16

17. Neither fat hands nor inconsistency preclude
causation
• Adiposity and many other physiologic states have both fat hand problems and
consistency problems
• This doesn’t mean they are not “causes”
• Consistency violations occur because we do not fully understand or have not
measured the relevant aspect of the exposure. Better research may allow us
clarify the specific exposure of interest.
• Fat hand interventions may not be a problem, depending what else is being
triggered by the intervention.
• Fat hand problems and consistency violations make it difficult to draw causal
inferences… but they don’t imply the exposure (or some flavor of the exposure)
is not causal
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