PHSSR IG CyberSeminar
Introductory Remarks
Bryan Dowd
Division of Health Policy and Management
School of Public Health
University of Minnesota
Causation versus Association
Who Cares?
• The purpose of public health systems and
services research is to examine the impact of the
organization, financing, and delivery of public
health services at the local, state, and national
levels on population health.
•By “impact,” I assume we mean the causal effect
on population health of changing one of those
factors.
Causation versus Association
• Linking “impact” and “causal effect” to change
highlights a common distinction between causation
and association.
• “Association” (the weaker term) often refers to
relationships among variables whose observed values
come from a single observation of each subject
(cross-sectional data).
• “Causation” often refers to relationships among
variables whose observed values come from multiple
observations of each subject (time-series data).
Causation versus Association
But many analytic techniques are designed to draw
causal inferences from cross-sectional data.
And the fact that two variables change their values
over time is no guarantee that the change in one
variable caused the values of the other variable to
change.
Much of our empirical research attempts to
distinguish causal relationships from spurious
relationships.
But from a practical perspective …
In public health, there often comes a time when we
must act: choosing a particular course of action or
the status quo. Examples:
1. Should we impose a quarantine?
2. Should we inspect restaurants once a month or
once a decade?
3. Should we inoculate the population against a
particular disease?
Association = status quo?
The practical and empirical question is, “Do the data
support taking one specific course of action versus
an alternative?”
In that context, saying the policy variable (that we
control) and the outcome variable (that we are trying
to influence) are merely associated, but not causally
related, is equivalent to answering, “No. We should
not take a particular course of action.” So
“association” often is synonymous with “stay the
course” or “maintain the status quo.”
But that’s illogical !
“Staying the course” when the data do not support
causal links between the policy variable and the
outcome is illogical.
If we can’t establish a causal relationship between the
policy variable and the outcome of interest, then we
have no way of knowing whether “staying the
course” will continue to be “associated with” the
same value of the outcome variable that it is now.
The bottom line …
We may speak of “association” but we always act as
though we have drawn valid causal inferences, even
when we choose not to change anything.
So the most important question about causal
inference is not how to pretend we don’t draw them,
but how to make them as reliable as possible so that
we make good decisions.
The Basic Research Question

X
Y
What would happen to Y if we were to change X by one
unit (sometimes we add, “… holding the effect of other
variables constant?”) X could be continuous (e.g.,
income) or binary (e.g., treatment versus control group).
Sometimes called the “marginal effect” of X on Y, which
we could denote β .
Challenges: Omitted variable bias

Enrollment in a
smoking cessation
program
Health outcome
Family health history
Omitted variable, Omitted Confounder, Spurious Correlation
Challenges: Mediating Variables

Education
Health outcome
Income
Controlling for income in a regression of health outcomes on
education means that β is the partial effect of education on
health outcomes, not the full effect. Which one do you want to
estimate?
Challenges: Reverse Causality
1
Health Insurance
Health Status
2
Reverse causality
We hypothesize that having health insurance affects health
status, but the relationship we observe could be due, at least
in part, to the reverse effect (medical underwriting).
What Methodologies?
What methodologies can be used to assess causal
relationships in HSR and PHSSR?
What forms of manipulation of the policy variables
of interest can produce reliable causal inference?
This is the area of great disagreement. When and
why did that disagreement occur?
A Brief History
Regression &
Correlation
Multivariate regression &
Partial correlation
Time
Experimental data: Galton,
Pearson, Fisher
Randomized
trials
Propensity scores
DAGs
Natural experiments
Observational data:
Wright
The “big split”
in 1926 - 1928
Structural equation modeling
Panel data
(Granger
causality,
etc.)
Instrumental
variables
Sample
selection
models
A Brief History
1926 - Ronald Fisher. Randomization. We (the
analyst) must manipulate the policy variables. A
research design solution.
1928 - Philip Wright. Instrumental variable
estimation. Other types of manipulation can
produce valid causal inference. A modeling solution.
Many social scientists still are reluctant to use the
word “causal” to describe their causal models.
Today
Today we have a broad menu of methods to choose
from, but residual resistance to using approaches
other than randomized trials.
Some estimation approaches:
1. Randomization
2. Instrumental variables
3. Natural experiments
4. Sample selection models
Example

Health department
characteristics or
programs (“interventions”)t-1
Past health problems
(unobserved)
Population health
outcomest
Community risk
factors (unobserved)
One Solution: Randomization

Randomization
Intervention
v
Outcome
u
Randomly assign health departments to interventions. Often
not practical, ethical or cost-effective.
Another Solution: Instrumental Variables
and Natural Experiments

External event
Intervention
v
Outcome
u
Some event external (“exogenous”) to the health department
(e.g., legislation, “encouragement”) that, like randomization,
results in the intervention being adopted by some
departments but not others, but has no direct effect on the
outcome.
Another Solution: Sample Selection Models

External event
Intervention
v
Outcome
ρ
u
Incorporate the correlation (ρ) of unobserved variables (v
and u) into the estimation of the causal parameter β. Same
data requirements for all methods. Estimation approaches
vary for different types of dependent variables.
Two Applications
The relationship between local public health
spending and measures of public health
outcomes.
The policy question: What is the effect of changing
the level of local public health spending on public
health outcomes?
Both authors recognize that local public health
departments were not randomized to different
levels of spending.