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 avasarala@yahoo.com

CAUSAL INFERENCE

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..

•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

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 LINKBETWEEN 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

OFGENERAL 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

OF INDUCTIVISM

-PREDOMINANTLY PSYCHOLOGICAL - SUBJECTIVE -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.

• HEALTH RESEARCH IS MAINLY EMPIRICAL RESEARCH AND LESS THEORITICAL.

• HENCE IT DEPENDS ALMOST ENTIRELY ON INDUCTIVE REASONINING.

INDUCTIVE REASONING

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/

EVIDENCE

FORM PREDICTIONS

DEDUCITVE LOGIC

HYPOTHESIS

ACCEPT AS

YOUR OBSERVATION PREDICTIONS

COMAPARE WITH

PREDICTIONSOBSERVATION

IF AGREEING WITH

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

• 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

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)

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.

REJECTED BY DEDUCTIVISTS, DAVID HUME THAT INDUCTIVISM LACK LOGIC

FALSIFICATION THOERY WAS REJECTED BY SUSSSER & KUHN AS IT IS PESSIMISTIC

REJECTED BY KARL POPPER BY THEORY OF FALSIFICATION

SCIENTIFIC LAWS & FACTS ARE NOT KNOWN WITH CERTAININTY AND DEDUCTIVE LOGIC YEILDS CONCLUSION ONLY WHERE THEY ARE 100% CERTAIN

SCINTISTS JUDGEMENT REGARDING CASUALITY IS BETTER – SUSSER& BAYES

MILLS & BACON INDUCTIVISM

CONTRIBUTIONS & CONTRADICTIONS

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

ABDE

AFGHJK

ALH NPRS

AIOQTVYX

CASUAL CONSTELLATIONS

A= NECESSARY CAUSE ABCDE= CAUSAL COMPLEMENTBCDE=CAUSAL COMPONENTS

1 2 3 4

SWITCH (D)

WIRING (C)

BULB (B)

ELECTRICITY (A)

MYCO TB (A)

POVERTY B)

ILLITERACY (C)

POORHOUSING (D)

SMOKING (A) UNFILTERED CIGARETTES (B) 16YR DURATION (C) HOST SUSCEPTABILITY(D)

LIGHTING

TUBERCULOSIS

LUNG CANCER

A:NECESSARY

BCD:COMPONENTS

ABCD:COMPLEMENT

A: NECESSARY CAUSE

BCD: COMPONENTS

ABCD: COMPLEMENT

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

COULD IT BE DUE TO CONFOUNDING

COULD IT BE DUE TO CHANCE

COULD IT BE CAUSAL?

APPLY CHECK LIST AND MAKE JUDGMENT

(COURTESY: BASIC EPIDEMIOLOGY BY BEAGLEHOLE & KNOLL)

NO

NO

PROBABLY NOT

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. TEMPORALITY

2. STRENGTH

3. SPECIFICITY

4. CONSISTANCY

5. COHERENCE

6. BIOLOGICAL PLAUSIBILITY

7. 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 -Epidemiology-

principles & methods