1. The document outlines steps for conducting causal inference research including defining a hypothesis, study design, drawing a directed acyclic graph (DAG) to identify confounders, analyzing data while controlling for confounders, and making scientific inferences. 2. An example is provided on estimating associations between deworming, hygienic latrines, finished floors, and soil-transmitted helminth infections using a cross-sectional survey design. 3. Results show the adjusted prevalence ratios for single and combined exposures on deworming and household characteristics on infection, including any additive interaction effects.

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.