Description

Susser described the rules of inference that are used in epidemiology to establish causality. These can help to formalize the process of ruling in or ruling out a diagnosis. The author is from Columbia University in New York City.


 

Rules of inference:

(1) time order (page 118)

(2) strength of association (page 118)

(3) specificity (a) in effects of a cause and (b) in causes of an effect (page 120)

(4) consistency on replication (page 122)

(5) coherence in (a) theortical plausibility, (b) in biology, (c) in facts, and (d) in statistical analysis with a dose response (monotonic) (pages 124 to 126)

 

Findings strongly against causation:

(1) time order incompatible

(2) consistency on replication negative

(3) factual coherence incompatible

 

Findings against causation:

(1) no strength of association

(2) theoretical coherence implausible

(3) biology incoherent

 

Findings for causation:

(1) strength of association strong

(2) theoretical coherence plausible

(3) biology coherent

(4) factual coherence compatible

 

Findings strongly for causation:

(1) strength of association very strong

(2) specificity in causes of an effect high

(3) consistency on replication positive

(4) dose response (monotonic)

 


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