NON-TECHNICAL INTRODUCTION TO REGRESSION 7
Explaining Disease: Causes,Correlations and Mechanism
Article: Explaining Disease:Causes, Correlations and Mechanism
Step 2: About the Article
Article and Causal Claim
The article presents anexplanation of disease. It discusses the mechanism, causes andcorrelation. It asks the questions of why people get sick. Theexplanation is made based on the causal instantiation of network. Thecausal networking describes the relationship among several factorsit includes a hypothetical or observational obligation of dynamics topatient to who an explanation is made. The article discusses theinference from causation to correlation and thus integrating recentdiscussion on causal reasoning with the approaches in epidemiology tocomprehend causality of disease. In essence, this particularlyconcerns lung cancer and ulcers. Then, how the causal mechanisms arerepresented through the causal network can enhance the reasoningbehind causation and the correlation between.
The article also gives areasoning why a particular class of people gets particular diseasessince it is a causal network. Also, to establish causality, it shouldbe concise that the exposure of the interest came before the outcomeby a period steady with the biological mechanism proposed. Therefore,epidemiologist should ask the questions related to questions aboutthe time consequence and biologic reliability. The development ofbacterial theory for instance can be interpreted through causality.The presence of bacteria shows the correlation between bacterialinfection and ulcers will possibly result into incorrect studydesign. In addition, the alternative explanations made are caused byexcess acid as a result of antacid’s success in reducing thesymptoms of ulcers (Thagard,1998).Nevertheless, the attitude towards hypothesis of ulcers has beenshown to change abruptly when other researchers observed thesebacteria in stomach sample.
The fundamental questiondescribed is whether this bacteria causes ulcers and this requireattribution of H. pylori, the causality power to increase ulcersoccurrence. Nevertheless, earlier studies failed to establishcausality and thus failed to address the question of feasible optionscauses for ulcers. In another case, the investigators of lung cancerused the case control methods to rule out the best alternatives. Thiswas made by pairing patients with lung cancer
In this analysis, causality hasbeen disproved. The causal relation between lung cancer and thepatient was assessed through considering a positive correlationbetween the variables. It therefore means that, when the alternativesdo not occur independently, the relation may not reflect causalstatus of the variables. Independent variables are evaluated to because of effect and those that are waiting to be tested are affected(Thagard,1998). By failingto account for all realistic features influencing the modification inthe dependent variable, one cannot be sure of the causality to bemeasured. The most fundamental thing is to guarantee that that thistesting transpire under resembling feasible condition as when theaverage results were measured
Critique of the Claim
In this article, the inferencefrom correlation considered the alternative causes. Nevertheless, ithas been interpreted and misunderstood incorrectly as causation.There is a positive correlation of explaining the diseases. Thepresence of bacteria shows the correlation between bacterialinfection and ulcers will possibly result into incorrect studydesign. Therefore, the development of bacterial theory for instancecan be interpreted through causality. The correlation between ulcersand cancer does not mean that people with H.pyloriwill get lung ulcersand some people may have ulcers without necessarily havinginfections. The disease explanation is thought best as instantiationof causal network. The causality network describes the correlationbetween factors. In this article, information is based on thepositive relationship between delinquent behavior and cancer.
Nevertheless, this research doesnot illustrate causation part since the relationship between the twovariables can be down as a result of various factors. The fact thatthere were changes in the coefficient shows that one or more variablethat was omitted and included now are related to the initial evidencefor this attribution. The evidence is therefore weak. Nevertheless,the coefficient changes are small based on the standard error.Therefore, if I were to use the correlation studies such ascausation, it is not clear of what variable ceases the causationeffect on another variable. Reverse causation based on this articleshows a direction of causality contrary to a two-way relationship.For instance, contrastingly, the description above of theinterrelation between causes, mechanism and correlations gives thebasis of a defensive alternative account which is inclusive of anexplanation that involves a single cause, is deductive andstatistical.
Facebookhas a foreign audience. The audience is attracted to marketers andbrands around the world. Changes in the types of advertising andbrand page to gain monetary from the public have been experienced. InMeasuring Returns on investment, Facebook paraphrased a 35-minutetalk which was gaining likes with promotional ads. Nevertheless, thestudy made a rookie mistake. There are correlations between the likesand the users. However, the conclusion illustrates causality that maybe or not is there for the sales of the product were more than theinternal projections. In essence, this shows that it would have beeneasier to identify causality where the client would buy massive fromthe media. Nevertheless, they could make a push with Facebook in aparticular region.
There is more to learn abouteconometrics and analysis. The study can present deepercomprehension, and more understanding of the problems discussedherein include the presentation of why some claims are valid, andthus it can present knowledge of whether the claims herein are true.One issue inherent in causality is that a researcher cannot guaranteeparticular manipulation every time the variable was the underlyingreason for the supposed correlation and trends. The ideal experimentthat I would run would require remembering that with causality, it isnot possible to complete. Nevertheless, the magical figure thatbrings an excellent test is to ensure that the peers accept theresult through a well considered and strong experimental design thatcontains pilot studies to institute causality before plowing on amore expensive and complex research.
It is often complex toneutralize and isolate the influence of confounding variables indifficult variables. Exponentially, it is hard for a researcher tomake their statement based on the cause. Thus, any research programshould include measures that are used to set up causalityrelationship. It is relatively easy to institute causality since agood experimental design can standardize any possible bewilderingvariables. Temporal factor should be used to neutralize since most ofthe experiments involve the administration of treatment and makingobservations given the linear sequential correlation. For instance,dialoguing a sample of the people may imply whether they may take thegovernment accountable. The process of causality is a matter ofensuring that the possible influence of the values that are missingis reduced.
The approximation could consistof small modification in the design and revamp. It could be just adifferent strategy or an experiment with real data. The bestapproximation to the truth of a given is validity. The approach tocausal attribution includes identifying the evidence would beconsistent with the causal relationship and then analyzing andgathering data from different sources aimed to determine the evidencematches. The approach its foundation from change theory whetherelaborate on implicit or detail in policy or program.
Different data analysis andcollection techniques are used to assemble the evidence. It isimperative to combine several methods in a single entity impactassessment based on the level of certainty needed and the possiblecounter-explanations instituted. Additionally, evidence from previousevaluation and research can be used. For instance, the impactassessment in the article does not need to test each of the links incausal chain given the knowledge level from the previous research.
The third strategy that can beused for causality is to establish the feasible alternatives for realdata and gather it to see whether it can be ruled out. The approachis useful when the evidence available may be enough only to suggest arelationship but not a causality. The available options are inclusiveof modeling to investigate the alternative explanation throughstatistical analysis such as logistic regression or regression forconfounding factors. As the use of non-existent, systematic causalattribution in developing an evaluation of the impact is rare.
Thagard,P. (1998). Explaining disease: Correlations, causes, andmechanisms. Mindsand Machines, 8(1),61-78.