Transcript CAUSAL INFERENCE Dr. A. K. AVASARALA MBBS, M.D. PROFESSOR & HEAD DEPT OF COMMUNITY MEDICINE & EPIDEMIOLOGY PRATHIMA INSTITUTE OF MEDICAL SCIENCES, KARIMNAGAR, A.P. INDIA : +91505417 [email protected].

CAUSAL INFERENCE
Dr. A. K. AVASARALA MBBS, M.D.
PROFESSOR & HEAD
DEPT OF COMMUNITY MEDICINE &
EPIDEMIOLOGY
PRATHIMA INSTITUTE OF MEDICAL
SCIENCES, KARIMNAGAR, A.P.
INDIA : +91505417
[email protected]
CAUSAL INFERENCE
•IT IS AN INTELLIGENT WAY Of APPLYING COMMON
SENSE AND JUDGEMENT SCIENTIFICALLY FOR
CONNECTING THE CAUSE/ FACTOR WITH THE
EFFECT/ DISEASE AND INFERING THAT PARTICULAR
FACTOR IS THE CAUSE OF THAT PARTICULAR
EFFECT/ DISEASE
IT IS A TRIAL PROCESS AS NO EPIDEMIOLOGICAL
EXPERIMENT, EVEN EXPERIMENTAL ONES, CAN
PROVE OR ESTABLISH CAUSE-EFFECT RELATION
SHIP CENT PER CENT.
THIS CAUSAL ASSOCIATION CAN BE NEITHER PROVED NOR
ESTABLISHED CENT PER CENT..ONLY THE MAXIMUM
EXTENT OF PROBABILITY OF THEIR INTER-RELATIONSHIP
CAN BE EXPLAINED..
CAUSAL INFERENCE
• A PROCESS OF PROVING THAT A
PARTICULAR HYPOTHESIS IS A REAL
ONE AND CAUSAL i.e., ESTABLISHING
THE CAUSE AND EFFECT
RELATIONSHIP BETWEEN A
SUSPECTED FACTOR AND A DISEASE.
• DIRECT CAUSAL ASSOCIATION MEANS
THAT A FACTOR IS REALLY RELATED IN
CAUSING THAT PARTICULAR DISEASE.
• WHAT EVER AN ASOSIATION IS SEEN, ONE
HAS TO PROVE ULTIMATELY WHETHER IT IS
DIRECT CAUSAL ASSOSIATION OR NOT.
GETTING TO THE REAL LINK
BETWEEN THE CAUSE AND EFFECT
• BY WEEDING OUT OR EXCLUDING
VARIOUS FALSE LINKS AND
REACHING THE REAL LINK
• THIS PROCESS HAS TO BE DONE
WITHOUT ANY ERRORS, EITHER
SYSTEMATIC (BIASES) OR DUE TO
CHANCE.
CAUSAL INFERENCE
A
SPECIAL CASE
OF
GENERAL PROCESS
OF
SCIENTIFIC REASONING
WHAT IS SCIENTIFIC
REASONING?
CONTRIBUTORS FOR SCIENTIFIC
REASONING
• 17TH CENTURY – BACON-INDUCTIVIST
• 18TH CENTURY – DAVID HUME -- DEDUCTIVIST
•
THOMAS BAYES-- MAKES THE SCIENTIST
RESPONSIBLE
• 19TH CENTURY - JAMES STUART MILL-- INDUCTIVIST
• 20TH CENTURY –
KARL POPPER -- REFUTATIONIST
KUHN, LAKTOS-- REJECTED KARL
SUSSER
• 21ST CENTURY – BRITISH
ALL ARE USEFUL
PHILOSOPHERS & SCIENTIST JUDGEMENT
BETTER
INDUCTIVIST PHILOSOPHY
(BACON & J.S.MILL)
• INDUCTIVE REASONING BASED ON INTUSION
THAT EACH EVENT IS FOLLOWED BY AN EFFECT
(PRAGMATIC PHILOSOPHY)
• OBSERVATIONS DRAWN FROM HYPOTHESES ARE
CALLED INDUCTIONS
• CONCLUSIONS ARE DRAWN FROM INDUCTIONS
DISANDVANTAGES
-PREDOMINANTLY
PSYCHOLOGICAL
- SUBJECTIVE
OF INDUCTIVISM
-LACKS LOGICAL
-EXPLANATION
J. S. MILL’S FIVE CANNONS (1856)
METHODS OF INDUCTION
AGREEMENT: IF A FACTOR IS COMMON TO A NUMBER OF
DIFFERENT CIRCUMSTANCES, THAT ARE ASSOCIATED
WITH THE PRESENCE OF A DISEASE, THAT FACTOR MAY
BE THE CAUSE OF DISEASE – THAT MEANS THERE IS AN
AGREEMENT BETWEEN THE FACTOR AND THE DISEASE
UNDER DIFFERENT CIRCUMSTANCES.
DIFFERENCE: IF THE FREQUENCY OF A DISEASE IS
MARKEDLY DIFFERENT UNDER TWO DIFFERENT
CIRCUMSTANCES AND SOME FACTORS CAN BE
IDENTIFIED IN ONE CIRCUMSTANCE NOT IN OTHER, THEN
THE FACTOR OR ITS ABSENCE, MAY BE THE CAUSE OF
DISEASE.
CONCOMITANT VARIATION: FACTOR WHOSE
FREQUENCY OR STRENGTH VARIES WITH THAT OF THE
DISEASE, IT MAY BE THE CAUSE OF THE DISEASE.
INDUCTIVE REASONING
• HEALTH RESEARCH IS MAINLY
EMPIRICAL RESEARCH AND LESS
THEORITICAL.
• HENCE IT DEPENDS ALMOST
ENTIRELY ON INDUCTIVE
REASONINING.
INDUCTIVE PHILOSOPHY
•THE CONCLUSION DOES NOT NECESSARILY
FOLLOW FROM PREMISES OR EVIDENCE AS
IN THE CASE OF DEDUCTION.
•CONCLUSION IS MORE LIKELY TO BE VALID
IF THE PREMISES ARE TRUE i.e. THERE IS A
POSSIBILITY THAT THE PREMISES MAY BE
TRUE BUT THE CONCLUSIONS FALSE.
CHANCE, THEREFORE, MUST BE FULLY
ACCOUNTED FOR.
•IT MOVES FROM THE SPECIFIC TO THE
GENERAL- IT BUILDS.
DEDUCTIVE LOGIC OF
DAVID HUMES
FROM
PREMISES/
DEDUCITVE
LOGIC
FORM
PREDICTIONS
EVIDENCE
YOUR
OBSERVATION
OBSERVATION
COMAPARE
WITH
IF AGREEING
WITH
PREDICTIONS
PREDICTIONS
ACCEPT
AS
HYPOTHESIS
DEDUCTIVISM
IN DEDUCTION , THE CONCLUSION
NECESSARILY FOLLOWS FROM THE
PREMISES ( All “A” is “B”, All “B” is “C”,
therefore all “A” is “C”.).
• DEDUCTION MOVES FROM GENERAL
TO THE SPECIFIC AND DOES NOT
ALLOW FOR THE ELEMENT OF CHANCE
OR UNCERTAINITY.
• DEDUCTIVE INFERENCES, THEREFORE,
ARE SUITED TO THEORITICAL
RESEARCH
•
KARL POPPER’S REFUTATIONISM OR
FALSIFICATION THEORY
POPPER BELIEVED THAT SCIENTIFIC STATEMENTS
CAN NEVER BE PROVED OR ESTABLISHED AS TRUE
LOGICALLY AND SCIENCE ADVANCES BY A PROCESS
OF ELIMINATION CALLED CONJECTURE(GUESS) AND
REFUTATION(DENIAL OR PROVING AS WRONG)
• IF “H” IMPLIES “B” AND “B” IS FALSE,
THEN “H” MUST BE FALSE
• TRY TO DISPROVE , IF YOU CAN’T ACCECPT IT
•
EVEN 100 WHITE SWANS CANNOT PROVE THE
HYPOTHESIS THAT “ALL SWANS ARE WHITE”. BUT
JUST ONE NON –WHITE SWAN CAN DISPROVE IT.
THE CONDITION “IF” MAKES THE THEORY
UNCERTAIN AND TENTATIVE.
• SOUNDS PESSIMISTIC
•
BAYESIANISM
• BAYES MAKES THE SCIENTIST
RESPONSIBLE TO ASCERTAIN THE
DEGREE OF CERTAINITY OR PERSONAL
PROBABILITY TO THE ARGUMENT.
• SCIENTIST HIMSELF HAS TO DECIDE
ABOUT THE CERTAINITY BY ATTACHING
HIS OWN (FROM HIS PERSONAL
EXPERIENCE) PRIOR
PROBABILITIES(INITIAL CERTAINITIES)
AND POSTERIOR PROBABILITIES
(CONCLUDING CERTAINITIES)
EVENTHOUGH THEY ARE SUBJECTIVE.
CONTRIBUTIONS & CONTRADICTIONS
MILLS &
BACON
INDUCTIVISM
SCIENTIFIC LAWS
& FACTS ARE NOT
KNOWN WITH
CERTAININTY AND
DEDUCTIVE
LOGIC YEILDS
CONCLUSION
ONLY WHERE
THEY ARE 100%
CERTAIN
REJECTED BY
DEDUCTIVISTS,
DAVID HUME THAT
INDUCTIVISM
LACK LOGIC
SCINTISTS
JUDGEMENT
REGARDING
CASUALITY IS
BETTER –
SUSSER&
BAYES
REJECTED BY
KARL POPPER
BY THEORY OF
FALSIFICATION
FALSIFICATION
THOERY WAS
REJECTED BY
SUSSSER & KUHN AS
IT IS PESSIMISTIC
TENTATIVENESS OF OUR
KNOWLEDGE
• All the fruits of any scientific work,
epidemiological or of other disciplines,
are at best only the tentative formulations
of a description of nature. This
tentativeness of our knowledge does not
prevent us for practical applications but
should keep us skeptical ad critical, not
only of everyone else's work but our own
as well
(oxford)
PRESENT VIEWS
• Each philosophy has its own
advantages and limitations.
• Induction, deduction, falsification,
scientist’s opinion – all are worth trying
in appropriate circumstances with
finer judgment.
• Mills’ cannons are still often used in
the forming of the hypotheses.
WHAT IS A CAUSE?
It is an event, condition or characteristic,
that precedes the disease event and
without which the disease event wouldn’t
have occurred at all or until later date
What is necessary cause?
It is the principal cause in all causal
constellations with out which the disease
cannot occur even though other causes are
presented and operating
CAUSAL COMPONENTS
The causes other than necessary cause,
complementing or helping the necessary
cause are considered as components of the
cause.
These are set of causes necessary to
make a factor sufficient to cause the
disease . They just complement each other
and act as independent partners. They will
not loose their individual identity, there is no
change in their biological mechanisms.
Causal Complement:
Is entire set of factors or conditions and
each component complement each other
for causal co-action or joint action.
Sufficient cause:
It is a set of minimal (all necessary
conditions and events) conditions and
events that inevitably produce the
disease. Sufficient cause competes causal
mechanism and initiates the disease
CONCEPT OF NECESSARY CAUSE (A) &
SUFFICIENT CAUSE
DISEASE
AF
GH
JK
ABD
E
1
2
AIOQ
TVYX
ALH
NPRS
3
4
CASUAL
A= NECESSARY CAUSE ABCDE= CAUSAL COMPLEMENT
CONSTELLATIONS
BCDE=CAUSAL COMPONENTS
SWITCH
(D)
WIRING
(C)
BULB
(B)
A:NECESSARY
LIGHTING
ELECTRICITY (A)
MYCO TB
(A)
POVERTY
B)
ILLITERACY
BCD:COMPONENTS
ABCD:COMPLEMENT
A: NECESSARY CAUSE
TUBERCULOSIS
(C)
BCD: COMPONENTS
ABCD: COMPLEMENT
POORHOUSING (D)
SMOKING
(A)
UNFILTERED
CIGARETTES
(B)
16YR DURATION (C)
HOST
SUSCEPTABILITY(D)
LUNG CANCER
A: NECESSARY
CAUSE
BCD: COMPONENTS
ABCD: COMPLEMENT
CAUSAL INFERENCE EXCERCISE
• Whenever an association is seen, one
has to prove ultimately whether it is
direct and real causal association or
not. This process of proving is causal
inference
• All the non-causal statistical
associations as well as spurious ones
have to be eliminated
• This has to be done without errors of
selection, information, confounding
and chance)
OBSERVED ASSOCIATION
COULD IT BE SELECTION OR MEASUREMENT BIAS
NO
COULD IT BE DUE TO CONFOUNDING
NO
COULD IT BE DUE TO CHANCE
PROBABLY NOT
COULD IT BE CAUSAL?
APPLY CHECK LIST AND MAKE JUDGMENT
(COURTESY: BASIC EPIDEMIOLOGY BY BEAGLEHOLE & KNOLL)
CHECK FOR BIASES
• Is the observed association due to
improper selection? Is it due to defective
sampling? Is it due to the absence of
comparison group?
• Is it due imperfect measurement of the
cause or its amount of exposure?re there
any confounders like age, sex etc limiting
the validity
• Is it just due to chance?
• After excluding all biases including that
due chance, Hill’s checklist has to be
applied to judge the causality with caution
and proper judgment
HILL’S CHECKLIST FOR JUDGING
THE CAUSALITY
1.
2.
3.
4.
5.
6.
7.
TEMPORALITY
STRENGTH
SPECIFICITY
CONSISTANCY
COHERENCE
BIOLOGICAL PLAUSIBILITY
ANALOGY
SCOPE OF HILL’S CHECKLIST
• Not hard and fast rules
• Just additional qualities or aids to judge the
causality
• If any association is possessing these
qualities, it is more in favor of causality but
reverse is not true. An association even
without these qualities may also be causal.
• It is not necessary that all these qualities to
be present as it is occurs very rarely.
• Each of these qualities has to be looked for
first, If present its weight should be judged
individually and combined.
• Even a few of them, if present, will help in
causal judgment.
MAIN CONSIDERATIONS
• Temporal sequence of the association i.e.
whether the cause is preceding the effect or not,
has to be searched first. If it is present, it is more
in favor of causal association.
• Then the strength of the association (the relative
risk/ odd’s ratio and dose response relationship)
which decides the power of the association
between the cause and effect has to be
determined. If relative risk is high, the association
is more likely to be a causal one.
• If temporality is present , a case control study (in
urgent situations) or a cohort study (if time
permits) may be initiated to find out relative risk/
odd’s ratio and to test for the causal
association.
SUMMARY
• Causal inference is an intelligent scientific
interpretation exercise to know whether
observed relationship is real or not and it
is to be done without errors.
References :
•
Roger Detels, James Mc Even-Oxford Text
•
Book of Public Health
Brian Mac Mahan -Epidemiologyprinciples & methods