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WHO IS IN CHARGE OF CAUSATION? Olaf Dekkers/20-11-2014 Dept Clinical Epidemiology Aarhus and Leiden.

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Presentation on theme: "WHO IS IN CHARGE OF CAUSATION? Olaf Dekkers/20-11-2014 Dept Clinical Epidemiology Aarhus and Leiden."— Presentation transcript:

1 WHO IS IN CHARGE OF CAUSATION? Olaf Dekkers/ Dept Clinical Epidemiology Aarhus and Leiden

2 Le Malade Imaginere (1673), Act III

3 Basic problem I  We don’t usually see that x causes y  Causal judgement is based on inference

4 Basic problem II  Inferences can be wrong  Statistics do not differentiate between association and cuasation

5 Basic problem III  We are taught, over and over, to be sceptical  Why most research findings are false. Ioannidis 2005  Am J Epidemiology does not allow the use of the word ‘effect(s)’ to denote association(s) It may promise more than an observational study can deliver Only studies in which there is an intervention to change exposure can study causes It misleads the uncritical reader

6 So…  Making causal inferences in clinical research is not straightforward (old news)  But we should not refrain from searching for causes, as clinical research aims to improve health

7 Where do we stand today? Theories of causation -Regularity theories (Hume) -Probabilistic theories -Counterfactual theories -Interventionism

8 Counterfactual theory of causation

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10 Counterfactual theory cf Paul LA Counterfactual theories Intuitive interpretation: If it is the case that Y occurs after X and that Y does not occur in the absence of X, than X is a cause of Y More formally: “X causes Y because the counterfactual ‘if not X then not Y’ is true.”

11 “X causes Y”  Counterfactual thinking is intuitively appealing  Italy lost the game (Y), because the referee was not objective (X)  She was late (Y), because of the snow (X)  She/he fell deeply in love with him/her (Y), because of his beautiful eyes (X)  Attacking Pearl Harbor (X), caused the USA to enter WWII (Y)

12 Why do we need causation theories?  What is a philosophical theory of causation giving an account for?  The theory facilitates causal inference, and gives an answer to the question how we can know whether X is a cause of Y (epistemic notion) In epidemiology the epistemic notion is more relevant  The theory might facilitate our understanding of what causation (in general) stands for (semantic/ontologic notion)

13 Epistemic notion of causation  Epistemic notion  Obesity (x) is a cause of mortality (y) because the counterfactual ‘if not X then not Y’ is true (in a least one person) (Counterfactual theory)  Obesity (x) is a cause of mortality (y) because being obese increases mortality probability (Probability theory)

14 Ontologic notion of causation  Semantic/ontologic notion  There are no working forces, there are only probabilities (Probability theory)  This notion is in a counterfactual framework difficult as the theory refers to non-occurring situations (or even worse: non-existing worlds)

15 Counterfactuals: philosophical problems  Pre-emption (see next slide) Counterfactual dependence is not a necessary condition for causation  Prevented causes Think of a new extremely powerful weapon (X) causing world-destruction (Y). Causation cannot be judged in a counterfactual framework  Many irrelevant causes Counterfactual dependence is not a sufficient condition for causation

16 Pre-emption Smoking Hypertension AMI

17 An unstable christmas tree Counterfactual theory needs many auxillary theories to keep it upright

18 Counterfactuals in epidemiology  What makes the theory so appealing in epidemiology?  Counterfactuals point towards non-existing situations: ‘If not X, etc’  In medical research counterfactuals do not exist  Even not in cross-over studies  But: we are used to control groups  Control group as approximation for the counterfactual  Counterfactual thinking resembles the way we do studies

19 Counterfactual in epidemiology BUT  No practical decision rule to infer causality  Refers to non-occurring situations  There is however a set of basic assumptions for causal inference in the counterfactual framework (cf Hernan):  ‘Exchangeability’  ‘Positivity’  X=1 and X=0 (exposure contrast) should be well-defined

20 Counterfactuals and exchangeability Patients (X=1) Outcome Y=y|X=1 Patients (X=0) Outcome Y=y|X=0

21 Counterfactuals and exchangeability Patients (X=1) Outcome Y=y|X=1 Patients (X=0) Outcome Y=y|X=0 Counterfactual definition of causation: If it is the case that Y occurs after X and that Y does not occur in the absence of X, than X is a cause of Y This can be inferred from a comparison under exchangeability: Two groups would have had an identical outcome (Y=y) if the exposure (X=1 or X=0) would have been the same

22 Exchangeability  Two groups would have had an identical outcome (Y=y) if the exposure (X=1 or X=0) would be the same  If the baseline prognosis is the same, then a difference in outcome between groups has a causal interpretation  This is threatened by confounding in observational studies

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24 Confounding and selection-bias  Causal question Z in an observational study  Epidemiological answer: bias and confounding unlikely  Then we are (philosophically speaking) referring to a counterfactual theory of causation

25 RCTs: a counterfactual paradigm

26 A RCT

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28 DREAM

29 RCTs  The randomized design fits the counterfactual framework  The control group approaches the counterfactual situation by design (randomization)  It is deprived from theory

30 RCTs  The randomized design fits the counterfactual framework  It gives decision rule for causation:  if a treatment x is randomized, then a difference in outcome Y between two groups has logically a causal interpretation because the counterfactual statement holds

31 RCT: a counterfactual paradigm  The counterfactual model and the RCT fit well  Stop here: don’t move philosophically further

32 Epistemic reduction?  The counterfactual model and the RCT fit well  That does not mean that we can only have causal inference in case of randomization  Reason: the counterfactual model is (as we have seen) neither sufficient nor necessary to infer causality

33 Ontologic reduction?  The counterfactual model and the RCT fit well  A reason for some to accept only interventions as true causes (interventionism)  This does not mean that there is only causation in case of a (randomized) intervention  Things you can hardly intervene like SES

34 Interventionism  Makes causation human-like  Many phenomenon difficult to capture in an interventionist framework  Big Bang  Volcano Eruptions  Postulation of hypothetical interventions (which do not differ from counterfactuals)

35 Who is in charge of causation?

36 Where do we stand today?

37 What do we believe?  Option 1: Roziglitazone reduces blood glucose level because the counterfactual (if not X then not Y) is true?  Option 2: Roziglitazone reduces blood glucose level because of an intrinsic drug capacity to do so?

38 Counterfactual theory presupposes causation  If we adopt option 2:  The counterfactual notion is true because x is a cause of y, and not the other way round  Counterfactual thinking can still be used to judge causality as it is be derived from causality  But: it can not be the only argument for causation

39 How foolish are we today?  This was not shown in a RCT  We have no ultimate counterfactual proof

40 How foolish are we today? JAMA 1982  RR roken – myocardinfarct = 2.0  Risico normaliseert niet na weghalen risicofactor

41 We (researchers) have to do more  There are reasons to think that risk factors and drugs have some inherent effects  But we have to do more for causal inference  Experiments (thinking along the lines of counterfactual theories is helpful)  Explaining why x causes y  Stating the conditions under which x causes y  This means that the philosopher is not in charge of causation

42 In summary  Counterfactual thinking fits epidemiology, probably because we use preferably a controlled study design  Counterfactuals can be helpful when thinking how to design/perform/analyze a study  Causality can not be reduced to counterfactual dependence, and causal inference requires more than counterfactual dependence


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