Evidence in Medicine: Correlation and Causation – Science-Based Medicine
This lesson explores the relationship between cause and effect and teaches you about the criteria for establishing a causal relationship, the. People often say: Correlation does not imply causality In this statement, the term correlation should be understood to mean “any systematic relationship. Causality is what connects one process (the cause) with another process or state (the effect), Physics; Engineering; Biology, medicine and epidemiology; .. When experimental interventions are infeasible or illegal, the derivation of cause effect relationship from observational studies must rest on .
Pseudoscientific proponents, on the other hand, praise science, they just do it wrong.
Cause and Effect: Mechanism and Explanation | catchsomeair.us
In reality there is a continuum along a spectrum from complete pseudoscience to pristine science, and no clear demarcation in the middle.
Individual studies vary along this spectrum as well — there are different kinds of evidence, each with its own strengths and weaknesses, and there are no perfect studies. Further, when evaluating any question in medicine, the literature the totality of all those individual studies rarely points uniformly to a single answer.
These multiple overlapping continua of scientific quality create the potential to make just about any claim seem scientific simply by how the evidence is interpreted. Also, even a modest bias can lead to emphasizing certain pieces of evidence over others, leading to conclusions which seem scientific but are unreliable. Also, proponents can easily begin with a desired conclusion, and then back fill the evidence to suit their needs rather than allowing the evidence to lead them to a conclusion.
For example, the anti-vaccine movement systematically endorses any piece of evidence that seems to support the conclusion that there is some correlation between vaccines and neurological injury. Meanwhile, they find ways to dismiss any evidence which fails to show such a connection. They, of course, accuse the scientific community of doing the same thing, and each side cites biases and conflicts in the other to explain the discrepancy.
It is no wonder the public is confused. How, then, do we use the evidence to arrive at reliable scientific conclusions? That is what I will be discussing in this series of posts, beginning with a discussion of correlation and causation, but here is a quick overview: SBM is achieved through a consideration of scientific plausibility and a systematic review of the clinical evidence.
In other words — all scientific evidence is considered in a fair and thorough manner, including basic science and clinical evidence, and placed in the context of what we know about how the world works. This leads us to the final continuum — the consensus of expert opinion based upon systematic reviews can either result in a solid and confident unanimous opinion, a reliable opinion with serious minority objections, a genuine controversy with no objective resolution, or simply the conclusion that we currently lack sufficient evidence and do not know the answer.
It can also lead, of course, to a solid consensus of expert opinion combined with a fake controversy manufactured by a group driven by ideology or greed and not science. Correlation and Causation Much of scientific evidence is based upon a correlation of variables — they tend to occur together. Scientists are careful to point out that correlation does not necessarily mean causation. The assumption that A causes B simply because A correlates with B is a logical fallacy — it is not a legitimate form of argument.
However, sometimes people commit the opposite fallacy — dismissing correlation entirely, as if it does not imply causation. This would dismiss a large swath of important scientific evidence. For example, the tobacco industry abused this fallacy to argue that simply because smoking correlates with lung cancer that does not mean that smoking causes lung cancer. The simple correlation is not enough to arrive at a conclusion of causation, but multiple correlations all triangulating on the conclusion that smoking causes lung cancer, combined with biological plausibility, does.
Correlation must always be put into perspective. There are two basic kinds of clinical scientific studies that may provide evidence of correlation — observational and experimental. Experimental studies are ones in which some intervention is given to a study population.
In experimental studies it is possible to control for many variables, and even reasonably isolate the variable of interest, and so correlation is a well-designed experimental study is very powerful, and we generally can assume cause and effect. If active treatment vs placebo correlates with a better outcome, then we interpret that as the treatment causing the improved outcome.
In observational studies populations are observed in the real world, but no intervention is being given.
Observational studies can be very powerful, because they can look as extremely large numbers of subjects more than is practical in an experimental study but the weakness is that all variables cannot be controlled for. Researchers can account for known variables race, age, and sex are commonbut it is always the unknown variables that can confound such studies. In observational studies lack of correlation is easier to interpret than a positive correlation — if there is no correlation between A and B then we can pretty much rule out a causal relationship.
The only caveat is that a correlation is being obscured by a factor that was not accounted for. When a correlation is found in observational studies — that is when the assumption of cause and effect must be avoided, and more thorough analysis is required. In this paper, we have proposed a novel approach, to extract cause—effect relationships in large time series data set of socioeconomic indicators. The method enhances the scope of relationship discovery to cause—effect relationships by identifying multiple causal structures such as binary, transitive, many to one and cyclic.
We use temporal association and temporal odds ratio to exclude noncausal association and to ensure the high reliability of discovered causal rules. We assess the method with both synthetic and real-world datasets. Our proposed method will help to build quantitative models to analyze socioeconomic processes by generating a precise cause—effect relationship between different economic indicators.
The outcome shows that the proposed method can effectively discover existing causality structure in large time series databases. Data mining, Cause—effect relationships, Causality, Temporal association, Temporal odds ratio Background A system such as mechanical, biological or social-economic system consists of independent components.
Australian Bureau of Statistics
These components influence one another to maintain their activity for the existence of a system in order to achieve the goal of the system. The system changes behavior when a component is changed or removed significantly.
This motivates us to find the reason or cause behind fault and discover the cause parameters in explaining the interactions among the components of a system or process.