5.3.5 causal inference in research

1. 42
1. Define research hypothesis
– Your hypothesis can include possible effect modification
– Determine to what extent you aim to make causal inferences
using your data
2. Determine study design (trial, cohort, etc.)
3. Draw a DAG
a. Identify potential confounders
b. Choose which variables to measure
4. Analyze your data
a.
b.
c.
Control for confounders identified in step 3
Assess effect modification on the additive or multiplicative
scale
Make statistical inferences
5. Make scientific inferences about your hypothesis
Causal inference in your
research

2. Example: worm infections

3. Background

4. Photo credit: http://charityworld.com/launching-‐
campaign-‐for-‐deworming-‐children/
http://www.waterplc.com/pages/community/charities-‐we-‐support/
wateraid/
http://www.tslr.net/2007_07_01_archive.html

5. 46
Example: worm infections
• Research question: estimate the separate and combined
associations of deworming, hygienic latrines, and finished
floors with soil-‐transmittedhelminth prevalence.
• Study design: cross-‐sectional survey
• Exposures: deworming, hygienic latrine access, finished floors
• Outcome: soil-‐transmittedhelminth infections

6. 47
Directed acyclic graph
Hygienic
latrine access
Deworming
STH
infection
Age/sex
Household
wealth
Cluster-level
wealth
Mother’s
education
level
Finished floor
Sub-district

7. STH prevalence with and without deworming
and finished floors
D -‐: no deworming
D + : deworming
F -‐: floor not finished (e.g. mud)
F + : finished floor (e.g. cement)

8. Table 4. Adjusted prevalence ratios for single and combined deworming and
household finished floor and relative excess risk due to interaction.
RERI>0 indicates interaction between deworming and finished floors on
the additive scale

9. 50
Causal inference across fields
–
–
–
–
–
–
• Causal inference is an active discussion topic and area of
development in numerous fields
• Epidemiology Biostatistics Economics
• Political science Sociology
• Psychology

10. 51
Food for thought
• “A cause is something that makes a difference.” Susser (1991). AJE;
133:635-‐48.
• “Whether it is the social or biological sciences, there may
be no real ‘gold standard’ for [understanding causality].”
Galea (2010). IJE; 39:97-‐106.
• “Good description is the bread and butter of good science
and good policy research” Berk, 2004. Regression Analysis: A Constructive Critique.
Thousand Oaks: Sage.

11. References
Benjamin-‐ChungJ, Nazneen A, Halder AK, et al. The Interaction of Deworming, Improved Sanitation, and
Household Flooring with Soil-‐TransmittedHelminth Infection in Rural Bangladesh. PLoS Negl Trop Dis. 2015 Dec
1;9(12):e0004256.
Berk RA (2004). Regression Analysis: A Constructive Critique. Thousand Oaks: Sage.
Galea S, Riddle M, Kaplan GA (2010). Causal Thinking and Complex System Approaches in Epidemiology. IJE;
39:97-‐106.
Hernan & Cole (2009). Invited Commentary: Causal Diagrams and Measurement Bias. American Journal of
Epidemiology 170(8).
Pearl J (2009). Causality: Models, Reasoning and Inference. 2nd Edition. New York: Cambridge University Press.
Pearl J, Glymour M, and Jewell N (2016). Casual Inference in Statistics: A Primer. 1st Edition. John Wiley & Sons.
Rothman KJ, Greenland S, Lash, TL (2008). Modern Epidemiology. 3rd Edition. Philadelphia: Lippincott Williams
&Wilkins.
Susser M (1991). What is a Cause and How Do We Know One? A Grammar for Pragmatic Epidemiology. AJE;
133:635-‐48.
VanderWeele (2009). Sufficient Cause Interactions and Statistical Interactions. Epidemiology 20:6-‐13.