Design Approaches to Causal Inference

1
Design Approaches to Causal Inference
Statistical mediation analysis answers the
following question, How does a researcher use
measures of the hypothetical intervening process
to increase the amount of information from a
research study? Another question is, What is
the best next study or studies to conduct after a
statistical mediation analysis to further test
mediation theory. Five general approaches (1)
double randomization, (2) blockage, (3)
enhancement, (4) purification, (5) pattern
matching for multiple variables, subgroups,
settings, time, and alternative manipulations
(Mark, 1986).
2
(1) Double Randomization
If the problem with the b path is that M is not
randomly assigned, then how about randomizing
both X in the X to M relation and randomizing M
in the M to Y relation. Say X was randomized and
there was a significant effect of X on M in Study
1. In Study 2, an experiment was set up so that M
was randomized to levels defined by how X changed
M in Study 1. If there was a significant relation
of M to Y in Study 2, then there is more evidence
for mediation.
3
Wood et al. (1974) Overview
Study of self-fulfilling prophecy in interviews
cited in Spencer et al., (2005). Race (X)
predicts quality of interview (M) and quality of
interview predicts performance (Y).
ConfederatePerson assisting with the
experiment. The confederates are used to
manipulate factors. Confederate applicants were
used in Study 1 for the X to M relation and
confederate interviewers were used in Study 2 for
the M to Y relation.
4
Wood et al., (1974)
Study 1. White participants interviewed either
Black or White confederate applicants (X). The
dependent variable M, was interview quality and
participants with Black confederate applicants
gave poorer quality interviews (M). Study 2.
Confederates gave either an interview (M) like
White applicants were interviewed in Study 1 or
like Black applicants in Study 1. This
manipulation had a significant effect on
applicant performance (Y). So randomization was
used for the X to M relation and the M to Y
relation.
5
Prevention Example (MacKinnon et al., 2002)
Norms increase exercise which decreases
depression. Study 1, X to M Similar to existing
prevention studies, participants either receive a
social norm manipulation to increase exercise or
not (X) and exercise is measured (M). Study 2, M
to Y Participants are randomly assigned to
conduct an amount of exercise (M) obtained in the
program group or the control from Study 1 and
depression is measured (Y).
6
Double Randomization Problems
Most problems center around the randomization of
the mediator so that it corresponds to the change
in the mediator in the X to M study. Study 2 is a
mediation model with a manipulation (X) that
should change M in the same way as X changed M in
Study 1. So Study 2 data is analyzed with
statistical mediation analysis with the same
problems of interpretation.
7
(2) Blockage Designs
The goal of blockage designs is to test a
mediation relation with a manipulation that
blocks the mediator from operating. For example,
lets say that an exercise program appears to
reduce depression by increasing endorphin
levels-- the hypothesized mediator. A blockage
manipulation would administer a drug to prevent
endorphin production so that persons receiving
the exercise program would no longer experience
reduced depression if the endorphin level is the
mediator.
8
(3) Enhancement Designs
The goal of enhancement designs is to deliver
interventions that enhance the effects of a
hypothesized mediator. For example, lets say that
an addiction treatment program reduces remission
by improving social support. An enhancement
design would increase social support even more to
demonstrate a larger effect on remission. Social
support may be increased by more exposure to a
therapist, additional contact with friends and
family etc.
9
(4) Purification Designs
The goal of purification designs is to reduce a
manipulation to its critical ingredients. For
example, in drug prevention research, it appears
that changes in norms, beliefs about positive
consequences of drugs, and intentions to avoid
drugs appear to be important mediators of drug
prevention programs. A purification design would
retain only those program components that address
these mediators to test whether the purer program
changes drug use.
10
(5) Pattern Matching
The goal of pattern matching is to specify
patterns of results based on mediation theory.
Different types of studies and information are
used to assess whether the pattern of results is
consistent with mediation theory. Multiple
variables a mediation relation is observed for
one variable but not another. For example, change
in beliefs about positive consequences of alcohol
use is a mediator for alcohol use but not for
tobacco use. Changes in beliefs about positive
consequences is a statistical mediator but
changes in beliefs about negative consequences is
not.
11
More Pattern Matching Examples
Moderators For example, prevention program
effects are most effective for persons low on the
mediator at baseline. Setting An intervention to
change norms to change behavior should be more
successful in a setting where more norm change
may occur. Different Manipulations A different
manipulation that should change the same
theoretical mediator should lead to the same
results.
12
Goals of CAPS Presentation

• Describe many mediating variable examples.
• Describe reasons for mediation analysis--it can
  help improve prevention programs and reduce their
  cost. It is also useful for testing theories.
• Describe the latest methods to assess mediation.
• Describe limitations of mediation analysis.
• Describe experimental as well as non-experimental
  designs to investigate mediating variables.

13
Summary of Workshop
Described methods for multilevel, categorical,
longitudinal, and multiple mediator data.
Moderators and potential designs to assess
mediation were discussed. New methods have more
power and are more accurate than older methods,
e.g., distribution of the product
methods. Mediation can be investigated in the
analysis of any design that includes mediating
variable measures. Mediation analysis provides a
way to extract more information from a research
study, e.g., action theory and conceptual theory.
Can improve programs.