Causation in Epidemiology: Unveiling the Complex Interplay

Causation Seminar J rn Olsen 20 November, 2014

Causes the C-word in epidemiology We study associations but we estimate effects because we are interested in causes AXIOMS 1. (Some) diseases have causes 2. Some of these causes are avoidable 3. Therefore some diseases are preventable 2

But what is causation? How do causes operate? And how can they be detected? First the history: Hume Mackie - Rothman 3

Hume one object E followed by another object D E D rarely if ever seen E D the necessary part - circular if the exposure is part of the definition of the diseases E D the sufficient part seen but not often 4

And monocausal effects are rare, even a simple press the switch, the light is on - has a sequence of causes from distal to proximal and Causes act in a probalistic manner, why? Causes usually have delayed effects, why? 5

Mackie Rothman causal model explains why causes act probalistic. Asb. G2 Smoke Smoke G3 asb G1 The probability of getting L.C. if you smoke is a function of the frequency of other component causes Asb, G1 and G2. 6

The induction time is the time it takes to complete a causal field and latency time is the time it takes for the disease to surface to clinical detection. INUS conditions. Is the model true? More important is does it work? Inspire to better understanding, more research. Shows why etiologic fractions sum up to more than 100% 7

Gwas studies indicate a very complex genetic set of causes perhaps that is why we see so few gene-environmental interactions. Problems in meta-analyses. No reason to expect simple additive or multiplicative effects 8

Counterfactual E D Hume s second definition. Or, in other words, where the first object had not been the second would never exist. Leads to counterfactual analysis of causation and a possible definition of causation. Leads also to the RCT and other designs that can provide the expected disease occurence among exposed had they not been exposed 9

Current definition of causation E is causing D if E increases the probability of D Or like E is causing D if E blocks the estrogen receptor leading to ... D in the presence of E Epidemiology versus toxicology in public health 10

If E is a cause of D in a population it must be true for at least one person that he or she would not have had this disease at this time if he or she had not been exposed. 11

Counterfactual reasoning implies we can imagine a world where the exposure is not present. Counterfactual reasoning also tells us why we do not compare cases and controls in a case- control study. We try to reconstruct the IR among exposed and not exposed. We try to imagine what would be the disease occurrence among the exposed had they not been exposed and we use not exposed for that well knowing that they are surrogates for counterfactual observation time. 12

Causes are upstream or downstream. Many sexual contacts cause cervical cancer? Cervical cancer is caused by a specific variant of HPV? Both levels of causation can be used in prevention. Down stream causes may become rather trivial accident research for example 13

Causes may be presented graphically as done by using DAGs. Put simply, causality has been mathematized. Judea Pearl: Causality. Cambridge University Press, 2010. 14

DAGs also illustrate situations where causal inference is justified. A randomizes trial, incl. Mendelian Randomiza-tion. R E D u Nothing feeds into R and R only affects E (incl. a gene?). No backdoor path closed at E. If R is associated with D there is a causal link between E and D. 15

Causal types E1 G1 G2 G3 E2 E3 People without G1,G 2,G3 (2 out of 3) People with G 2,and G3 People with G1 are immune are doomed are causative 16

Quantification of causes Even if diseases have many causes OK to focus on one. The causes of interest are those that can be manipulated F lling s Disease. 17

Do we need a concept of causation? The surgeons point of view If someone kicks me in the back and it hurts I know what causes the pain and I want revenge - not a lecture in philosophy Use causal concepts but leave the nature of causation to philosophers? Perhaps but leaves much of the basic theory in the dark. 18

Causal interpretation problem (CIP) Alex Broadbent 19

If nobody in the population smoked the risk of L.C. in that population would be 50% less - But estimate is too high what would replace smoking? 20

Counterfactual; a cause makes a difference: Had it been different or absent, then the effect would have been different or absent (AB). 21

A measure of causal strength is a measure of the net difference in outcome explained by the exposure. To explain a difference in outcome such that the outcome in group A is greater than the outcome in group B by degree, u , we must have a difference in exposure which could at least explain this degree, u. (AB). Strength of association is a causal criterium 22

A result, claim, theory, inference, or other scientific output is stable if and only if a) It is not known to be contradicted by good scientific evidence and b) Given best current scientific knowledge, it would probably not be seen contradicted by good scientific evidence, if good research were to be done on the topic (AB) 23

The What could possibly go wrong? When you have eliminated all other explanations what remains must be the truth. Sherlock Holmes 24

Conclusions The Mackie/Rothman causal model has been useful in providing an understanding (true or false) of many of the concepts we use. It inspires to new theories and hypotheses. DAGs has been useful in understanding how causes operate and in helping separating out causal paths of interest from causal paths of no interest. 25

Counterfactuals has focused our attention on what we aim at in our designs and analyses. Cross over design, case-only designs, sib studies etc. not only RCTs. 26