Transcript Casual Inference and Propensity Scores

Everything is Missing… Data
A primer on causal inference and
propensity scores
Dan Chateau
MCHP Houses the Anonymized
Population Health Research Data Repository
Income
Assistance
CancerCare
Social
Housing
Education
Healthy
Child MB
Justice
Family
Services
Hospital
Pharmaceuticals
Home Care
PopulationBased
Health
Registry
ER
Vital
Statistics
Provider
• Families First
• Healthy Baby
• EDI
Immunization
Medical
Services
Lab
Nursing
Home
Clinical
Health Links
Census
Data at
DA/EA Level
• K to Grade 12
• Post-Secondary
(UofM)
• ICU
• FASD
• Pediatric
Diabetes
How do we know if something worked?
Ideally we have results from both
worlds…
alternate realities if you will
C
A
B
treated
whole
world
treated
untreated
compare
whole world
untreated
The Propensity Score--Review
• Predict the likelihood of exposure…
And
• Match on that
• Use Inverse Probability of Treatment Weights
The Propensity Score--Review
Assess: Did propensity score create comparable groups?
• Distribution of covariates in Group 1 comparable to distribution
of covariates in Group 2?
Maternal Age at First Birth
Alcohol Use
Antisocial Dad
Antisocial Mom
Maternal Anxiety
Child abuse Mom
Maternal Depression
Maternal Type II Diabetes
Drug use
Family disability
Social Isolation
Low education-Mother
Mentally disabled Mom
No prenatal care
Relation distress
Maternal Schizophrenia
Screened Prenatally
Socio Economic Status:
SEFI2
Single parent
Smoke during pregnancy
Social assistance
Maternal Substance Abuse
Violence
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
The Propensity Score--Review
Assess: Did propensity score create comparable groups?
• Distribution of covariates in Group 1 comparable to distribution
of covariates in Group 2?
• This and tests on higher moments suggested comparable
• Assess results
Can we hang our hat on the results?
• Likely, there exists some unmeasured confounding.
• How much confounding is needed to nullify our findings?
CONFOUNDER Impact of variable
Not Significant
STRENGTH OF CONFOUNDER
Can we hang our hat on the results?
• Likely, there exists some unmeasured confounding.
• How much confounding is needed to nullify our findings?
CONFOUNDER
Not Significant
STRENGTH OF CONFOUNDER
Impact variable
Can we hang our hat on the results?
• Likely, there exists some unmeasured confounding.
• How much confounding is needed to nullify our findings?
CONFOUNDER
Impact on LBW
Not Significant
STRENGTH OF CONFOUNDER
Can we hang our hat on these results?
• Sensitivity Test quantifies the strength of this
unmeasured confounding
• How strong of a confounder will nullify findings?
– If a strong confounder is needed: robust to confounding
– If a weak confounder is needed: sensitive to confounding
• Strength is a function of two things:
– Size of the relationship Benefit LBW
– Precision of the relationship Benefit LBW
Rosenbaum P. Observational Studies. 2nd ed. New York, NY: Springer-Verlag New York, Inc., 2010.
Guo S, Fraser MW. Propensity Score Analysis: Statistical Methods and Applications. Sage Publications,
2009.
Jiang M, Foster EM, Gibson-Davis CM. Breastfeeding and the Child Cognitive Outcomes: A Propensity
Score Matching Approach. Maternal and Child Health Journal 2011;15:1296-1307.
Can we hang our hat on these results?
Without Healthy Baby Benefit
• Low-Income LBW rate HIGHER than High-Income LBW rate
With Healthy Baby Benefit
• Low-Income LBW rate LOWER than High-Income LBW rate
Inequality with and without benefit: Significantly
Different
• Need confounder that accounts for 26% of this relationship
• Over and above balancing achieved through propensity score
• Is it likely that such a confounder exists?
Thank You / Questions
• umanitoba.ca/centres/mchp
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• twitter.com/mchp_umanitoba (@mchp_umanitoba)