In the post for Assignment A10: Causal Argument, I’ve provided several of you specific recommendations you might find helpful in crafting Causal Arguments for your research topics. While you put your arguments together, decide what sort of framework suits your argument best:
Single Cause with a Single Effect (X causes Y)
“Facebook Can Cost Us Our Jobs”
The premise is that something supposedly personal, about which our employers should have nothing to say, is nevertheless available to our employers, and to prospective employers, if we make it so. What needs to be proved is that information about our non-work lives, or information we post to Facebook about our work lives, can keep us from getting a job, from advancing in a job, or from keeping a job.
- You may say that sounds illegal or unethical, but your objection is irrelevant to the causal argument.
- You could examine how different professions handle social media differently (for example kindergarten teachers might be fired for indiscretions that wouldn’t cost an insurance agent her job), because your topic is still what costs the teacher and the agent their jobs.
- You could argue that free speech should be protected if it’s true, and nobody should be fired for saying his boss cheats on his wife, but your objection is irrelevant unless there really are certain types of speech for which we can’t be fired and types for which we can (X causes Z, but Y does not cause Z).
- You could certainly make a good argument that employers have different policies regarding social media activities of their employees (X causes Y at Company 1, while X causes Z at Company 2).
Single Cause with Several Effects (X causes Y and Z)
“We Are the Casualties of the War on Drugs”
The premise is that the War on Drugs has been counterproductive, subjecting the nation to increased drug use and drug-related death. What needs to be proved is that government interference in drug production and distribution creates crime, interrupts quality control, causes disease, and kills users, traffickers, and innocent bystanders of the illicit drug trade.
- You could argue that the prohibition of certain desirable substances leads inevitably to a frenzied underground and by definition criminal enterprise to meet the demand.
- You could argue that criminals aren’t always scrupulous about the quality of the contraband they deliver and that their product often harms or kills.
- You could point out the countless people languishing in jails for owning small amounts of something that used to be legal.
- You might want to mention that drug use, even sanctioned use of safe prescription drugs, can be very detrimental in and of itself, but your comments would be completely irrelevant to the causal argument.
- You might also want to say that drug dealers get what’s coming to them when they deal in illicit materials and it’s wrong to blame cops for killing them, but again, that’s irrelevant to the question of whether the War on Drugs results in death.
Several Causes for a Single Effect (Both X and Y cause Z)
“There’s No One Explanation for Gangs”
The premise is usually employed to refute the “common knowledge” that a single cause can be blamed for an effect. If you’ve chosen a topic about which everybody “knows” the cause and effect, your causal essay will dispute the notion that there is in fact a single cause.
- You could produce evidence that gangs are more prevalent in public housing projects than in suburban neighborhoods, but with special care. You still won’t have identified the cause, only the location of the cause.
- You could produce evidence that a large majority of the kids in gangs come from families without a present, positive, male role model, but with great care in how you describe the situation, to avoid using misleading shortcuts like “kids with no dads.”
- You could describe gangs as often engaged in petty criminal activity or as pointlessly obsessed with territorial disputes, but it’s completely irrelevant to your causal argument to describe what happens after a kid is in the gang when you intend to prove why he joined it in the first place.
A Causal Chain (X causes Y, which causes Z)
“Failure to Prosecute Rape Causes Rape”
The premise is that rape occurs because it’s tolerated and that every resulting rape reinforces the sense that it will be tolerated. Rapes of female students on college campuses are routinely reported to campus authorities, not local police, and are kept from local law enforcement to protect the reputation of the school at the expense of the rights of the victim. What needs to be proved is that the rapes are in fact kept secret, that the assailants escape justice, and that there is local awareness that sexual assaults are not prosecuted or punished.
- You might want to investigate how it came to be that colleges got jurisdiction for sexual assaults on campus, but it’s probably irrelevant, unless you can demonstrate that they did so deliberately in order to keep assaults secret.
- You might want to explain what you think are contributing causes, such as the loss of bonuses or jobs for administrators on whose watch the public learned of campus rapes.
- You would need to argue that somehow, even though the outside world never hears of these rapes, students on campus learn that assault victims are not believed or supported and that assailants are not punished. This is essential to the chain.
- You could make a suggestion that if victims of rape refused to be “handled” by honor boards and campus judiciaries and took their cases to the local prosecutors instead they could break the chain. Arguing how to break the chain is a confirmation of why the chain continues.
Causation Fallacy (X does not cause Y)
“Violent Games Are Not the Missing Link”
The premise of this causation fallacy argument is nobody has yet proved a causal link between a steady diet of violent video games and actual physical violence in the lives of the gamers.
- You might be tempted to demonstrate that gamers are actually sweethearts who join the Boy Scouts and help old ladies across the street without knocking them down, but you don’t have to. You merely want to prove that they’re no more violent than players of other games.
- In fact, you don’t need to prove anything positive of your own to produce a strong causation fallacy argument; you only need to discredit the logic, the methods, or the premises of your opponents who think they have proved causation.
- For example, if an exhaustive study finds a strong link between kids who play violent video games and kids who kick their classmates on the playground, you argue this is mere correlation. It’s equally likely that the kids were violent first and attracted to the games as a result of their taste for aggression.
- You could also question the methodology of the supposed proof. If a questionnaire measures hostility, the answer: “I am suspicious of overly friendly strangers” no more proves hostility than it indicates a healthy wariness of the unknown.
In-class Exercise
As a Comment to this post, make a brief Causal Argument in reaction to the Advertising Failure reading. Start with an Effect and examine what Caused it. Or Start with a Cause and examine its Effects. For example:
- What caused the woeful 65% national “success” rate for radiologists reading mammograms?
- What caused Dr. Adcock to believe he could improve this horrible situation?
- What caused Kaiser Permanente to adopt the dangerous new strategy?
- What were the results of publishing the news internally for the radiologists to see?
- Name another before we start
- Name another before we start
- Name another before we start
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AnonymousStudent
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Caravan
cinderella1013
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gobirds17
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Gir
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Kenneth Alex Krug
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Counterintuitive Fall 2022 
Dr. Adcock’s team is missing one third fewer cancers and has achieved “as high a level of accuracy as mammograms can offer” because Dr. Adcock has fired employees who were missing cancers and the rest of his team is now on high alert to make sure that they do not miss any cancers so that they will not get fired too.
Dr. Kaiser’s poor success rate was caused by a lack of experience in the field and poor practice in the time where he was still learning.
Effect: Radiologists are getting more in-depth with examining their patients’ results.
Cause: New information shows that in the past radiologists missed several tumors due to their lack of followup and thoroughness.
The results of publishing the news internally for the radiologist to see is that other radiology programs are adopting Dr. Addock’s “batting average” method to hold their doctors accountable. This mean that not only the patients at Dr. Addock’s hospital are getting better results from their x-rays, patients from other programs are also getting better readings.
Kaiser Permanente needed an improvement to their department, and once Dr. Adcock’s solution started to result in positive results, Kaiser believed that the dangerous new strategy would provide a positive benefit.
I meant it more of:
Cause: Dr. Adcock’s dangerous solution started to outcome positive results.
Effect: Kaiser Permanente decides to adopt the strategy.
That’s much better.
Cause: women with breast cancer who go through a mammogram and have tumors go undetected due to the doctors lacking experience reading them.
Effect: 35% of women with breast cancer go undetected through mammogram.
Effect: Dr. Adcock forced his radiologist to go through tests to improve the proficiency rate of reading mammograms.
Effect: proficiency grows to only 20% undetected.
What was the cause of Anne Veenstra’s anger when finding out she had a tumor?
Veenstra’s original anger was because on her first attempt she was cleared of any tumors, and felt that the hospital should have found it the first time. The primary affect on her anger may have been the fact that she had a tumor.
Deficient training in combination with the competitive field of medicine result in inexperienced radiologist who are ill-equipped to handle mammograms.
Perhaps not, it was more of a stream of consciousness. I remember hearing about the highly competitive nature of the medical field, and as result some students will cheat in order to graduate and get into the practice. Your right, I didn’t elaborate, and I can’t prove that part of the claim with the given information.
I see what you mean now. You think doctors might not be as well-prepared as their degrees would indicate.
What caused Kaiser Permanente to adopt the dangerous new strategy?
In healthcare, the patient always is the first and only priority. Dr. Adcock and Kaiser clearly recognized this, and instead of keeping quiet the low percentage of diagnosed tumors in mammograms, decided to correct the discrepancies and alert women whose mammograms were false positives. This situation could have resulted in negative attention for Kaiser, but instead has resulted in positive reviews of the medical center. Patients are the priority of Kaiser and with a new team of practiced physicians, is able to keep those patients healthy.
What I believe caused some doctors to miss cancer in some mammograms is that that some doctors were reading to many mammograms. One doctor stated that he could read 200 before possibly seeing cancer. The process of reading mammograms is repetitious and when a doctor is reading upwards of fifty a day and not seeing anything the time there is cancer the doctor may miss it because he could simply just be going through the process again. If there were more doctors and so each could spend more time looking at each mammogram, there would be a higher success rate for catching cancer.
Well, that’s interesting. I wonder if 1 in 200 is an accurate average for cancer among mammogram patients.
About this sentence: “when a doctor is reading upwards of fifty a day and not seeing anything the time there is cancer the doctor may miss it.” Does it mean the doctor may miss cancer when it’s there?
What were the results of publishing the news internally for the radiologists to see?
This made the doctors feel that they were being assaulted on their autonomy and prestige. It became unfair when doctors claimed that the statistics were too easily twisted and infuriated them. It also exposed some doctors who were not able to preform their job properly. By publishing these scores, it eventually lead up to Adcock creating a more suited team and missing fewer and fewer tumors scanned in mammogram readings.
I want to say the doctors’ feelings of lost prestige are irrelevant, but those feelings are an effect, so I won’t object.
How does the publishing of a set of numbers “become” unfair? Does it develop from a fair program over time?
Exposing doctors doesn’t improve them, Taylor. Either they felt the shame and found ways to improve to avoid the shame, or they shifted away from film reading (both would help the averages).
Your last sentence has a big syntax problem in addition to its overall wordiness.
Upon ascending to his position as lead radiologist, Dr. Adcock had an opportunity to make a change. He believed that there were too many mistakes being made on the the behalf of the radiology team. His response was to adopt the new method of providing feedback to his fellow doctors and to the department itself. Through his thorough analysis of the films he concluded that some of his staff was not up to parr to reading these films accurately. Through his meticulous assessments of his staff he help improve the hospitals overall accuracy rate for mammographies.
You offer several cause/effect relationships here, Casmir. One that’s not clear: “analysis of the films” yields: “conclusion of inaccuracy.” What sort of looking at the films would lead to that conclusion?
I like an in-depth analysis of the several possible causes for a single effect, Benjamin. Looking at your scenario from Kaiser’s position though, you really only offer two choices, and then take one back. Nothing or something looks like the first choice. Nothing has very negative consequences. You offer as a further inducement for Something that it was a good strategy, but for Kaiser faced with the choice, that was not a given. Only after they decided on Something and let it play out did they find to their relief that it worked.
So the chain goes:
—Publish poor performance reviews
—Fear?
—Better performance?
Where’s the condition that poor performance would result in job loss?
See Luke’s comment above. Your chain goes:
—Doctors underperform
—Underperforming doctors are fired
—Remaining doctors fear the results of underperforming
—Doctors raise performance
whoops, I didn’t mean to re-state someone else’s thought. How about this: Dr. Walsh was said to have made improvements in his performance at reading mammograms by attending training sessions after he was fired from Kaiser for missing too many diagnoses.His improvements could be credited to the fact that he was made aware, though in an unfortunate way, of his multiple errors.
Very nice.
Mostly true. At the time of his firing though Walsh was the only doctor known to have misread too many mammograms. Later the group was discovered and THEIR firings or reassignments could be described in terms of “one of the doctors who. . . .”
This is the best writing you’ve done for us this semester, Saarah. Very nicely said. Allow me to revise just one sentence though, for illustration’s sake:
When Dr. Adcock looked at the numbers he apparently saw a “promise of revelation.” He saw an opportunity to hold doctors accountable for their diagnosis in a fair way. After releasing the weakest links in his staff, he assigned eight doctors to re-evaluate mammogram results. The close attention to the mammograms, and the sharpening of the doctors[‘] skills,
is what made the statisticschange[d the statistics] for the better. Now Dr. Adcock’s team misses one[-]third fewer cancers.Even better would be: The close attention to the mammograms, and the sharpening of the doctors’ skills, improved their statistics.
That’s thoughtful and detailed, Marcus. I’m going to try one of my own now that I have time to reflect and do so carefully.
—The federal minimum for mammogram readings is 480 per year.
—Doctors who read fewer than 480 lose their jobs.
—Doctors who read 500 keep their jobs, provided they do it well.
—The smallest sample that yields a statistically reliable analysis of job performance is (pick a number) 1000 per year.
Result: Doctors who read between 500 and 1000 keep their jobs even though their performance cannot be measured.
Result: Doctors of unproven ability are reading mammograms.
—Dr. Adcock wants to weed out 1) doctors of proven fallibility and 2) doctors whose fallibility rate is unknown (or unknowable)
Result: Doctors who read between 500 and 1000 films a year are fired or re-assigned.
Result: The remaining team can be accurately evaluated.
I like this exercise. Most of the process takes place in the head without the fingers, but typing the steps does help bring the details into focus. Good practice before writing any Causal writing.
This is unique. We haven’t examined the possibility that too many readings in a day could cause errors. Your analogy by comparison seems valid, but your conclusion that daily fatigue is somehow cumulative, with the unstated conclusion that mammographers get less accurate the longer they keep their jobs, is completely without basis in your evidence and seems a stretch.
The point you make that accuracy dips during the day is plenty worrisome. Push for too much more than that and you harm your credibility.
Reaction?
I completely agree. I could have just left it at ‘the accuracy will dip throughout the day.’ instead of claiming that it could have been cumulative. That claim is simply begging for too much. When considering the facts I’m pretty sure that there is evidence that proves my second claim, that the eye strain could accumulate over time wrong.
Question : in regards to the single cause with several effects. Are drugs themselves as a killer irrelevant to the causal argument because the argument is how the government interference kills users?
Yes, that’s exactly right. The thesis is that the War on Drugs is killing people. The fact that drugs would kill people even without a War on Drugs is entirely irrelevant to that thesis.
However, we could easily craft a thesis that makes the fact that drugs kill people entirely relevant. We could say, for example, that “the War on Drugs, though not entirely successful, has saved many lives.” We would support that thesis with the argument that the WOD has deterred many curious but timid people from taking drugs who might otherwise have tried them if they were legal, or if the government stopped prosecuting for possession and use. In doing so, the WOD has spared lives from overdose, from tainted drug deaths, from addiction leading to sickness and death, from suicides undertaken during withdrawal, even from deaths that result from interactions with dealers. All those types of deaths (some of which the WOD has prevented) result not from the WOD but from drug buying and use.
Helpful?