A town puts up new streetlights, and that same year burglaries drop.
The argument concludes the lights scared off the burglars, and it feels airtight.
It is also one of the most common ways the LSAT gets you to nod along at something
that proves nothing.
But I want to nod along.
The lights went up, crime went down.
What is actually wrong with connecting those two?
Hold that instinct, because by the end of this you will see at least three different
things that could be hiding behind that drop, and not one of them is the
streetlights.
Let me reach back a couple of episodes first.
Quick retrieval, all the way back to Episode 1 and then Episode 4.
When the LSAT asks you to weaken an argument, what are you actually attacking?
Not the premises, not the conclusion.
Take a second before I say it.
The assumption.
The bridge between the support and the conclusion.
We were poking the gap, the thing the author needs to be true but never said out
loud.
That is the whole game today, because causal arguments smuggle in one specific,
predictable assumption, and once you can name it, weaken and strengthen stop being
guesswork.
So here is the objective.
By the end of this episode, the second a stimulus concludes that X caused Y,
you will name the hidden assumption and run a three-question attack on it in under
fifteen seconds.
So this is not a brand-new question type.
It is a pattern that keeps showing up inside the types we already learned.
And that is exactly why it pays.
Logical Reasoning is now two of the scored sections, roughly two-thirds of the test,
and causal flaws are scattered all through it.
Learn this one shape and you are not fixing one question, you are fixing dozens.
Three moves today.
One, define a causal claim and find its gap.
Two, the three ways to attack it.
Three, how to defend it, which turns out to be the same three moves run backwards.
Picture two lines on a chart that rise and fall together.
Ice cream sales and, let's say, sunburns.
They move in lockstep all summer.
That togetherness has a name, and the name is correlation.
Now picture an arrow, one thing reaching out and producing the other.
That arrow is causation, and it points one direction only.
So the claim is not that the two things happened together.
The claim is that one of them made the other happen.
That is the whole distinction.
Causation is directional and asymmetric.
X to Y, never just X and Y side by side.
And here is the move the test runs on you.
In almost every LSAT causal argument, the correlation is the premise and the
cause-and-effect statement is the conclusion.
The author hands you two things moving together, then quietly leaps to one of them
causing the other.
And that leap is the gap from Episode 1.
It is the same gap wearing a costume.
Correlation does not equal causation is just a fancier way of saying there is a gap
between the premise and the conclusion.
So name the hidden assumption with me, because it is the spine of everything else.
When an author says X caused Y based on a correlation, the author is secretly
assuming three things at once.
That X is the only explanation for Y.
That the direction is right, X to Y and not the reverse.
And that it is not a fluke.
Defend any one of those and you strengthen.
Break any one of those and you weaken.
One pushback.
You said the only explanation.
That feels too strong.
Real authors do not always claim it that hard.
That is the right place to push, and you have caught a real limit.
The only-cause assumption holds when the author is fully certain,
plain correlation in, confident cause out.
But if the author hedges, says a contributing factor or partly responsible,
the assumption softens and your attack has to soften with it.
So check the author's confidence before you swing.
For the big, certain causal conclusions the test loves, though,
the three-part assumption is dead on.
Here is your checklist, three questions you run on every causal stimulus.
Question one.
Could something else have caused Y?
That is the alternative cause.
Back to the streetlights.
Suppose the town also added police patrols that same year.
If the patrols cut the burglaries, the lights get no credit.
The correlation survives, but the causal claim wobbles.
And there is a sneakier version of question one, isn't there.
The hidden third thing.
The cleanest example there is.
Picture a city where ice cream sales and drowning deaths rise and fall together all
summer.
It is tempting to say sugar makes people reckless near the water.
But there is a third variable sitting underneath both of them.
Heat.
Hot days drive ice cream sales up, and hot days drive people into the water,
where a few drown.
Ice cream and drowning correlate, but neither one causes the other.
A common cause drives both.
That third-variable move is just a flavor of alternative cause,
so keep it in the same bucket rather than counting it as a fourth attack.
That keeps the list short.
What is question two?
Question two.
Could it be backwards?
Reverse the causation.
People who drink coffee report sharper focus, so the argument says coffee improves
focus.
But flip the arrow.
Maybe the people who already need to focus, the students cramming,
the workers buried in deadlines, are the ones reaching for coffee in the first
place.
The need to focus caused the coffee, not the other way around.
Same correlation, opposite arrow.
This is the one I used to walk right past.
I would hunt for an alternative cause and never even ask whether the whole thing ran
the other direction.
Most students walk past it, which is precisely why the test plants it.
And there is a tiny tell that helps.
A cause has to come before its effect in time.
So if the so-called effect actually happened first, the arrow cannot point the way
the author wants, and you file that under reverse causation.
Question three.
Could it just be a fluke?
Coincidence, or bad data.
Back to the streetlights one last time.
Suppose burglaries fell across the entire state that year, every town,
lights or no lights.
Then this town's drop is just one ripple in a statewide wave,
and the streetlights were along for the ride.
So the three are: something else did it, it runs backwards,
or it is a coincidence.
That is the entire arsenal.
Alternative cause.
Reverse it.
Coincidence.
Three questions on every causal stimulus, and once you run them the credited answer
usually walks up and introduces itself.
Let me work one fully, then I hand you the last step.
A company rolls out a new wellness app.
It finds that employees who use the app take fewer sick days.
The conclusion: the app made them healthier.
Pause it right there.
Before I weaken it, which of the three attacks feels strongest here?
Try it yourself first.
I will commit, and I will defend it.
Reverse causation, easily.
The argument says the app made them healthier, but I think it is backwards.
Being healthier is what made them use the app.
Healthy people are the ones who bother with a wellness app,
so the health caused the app, not the app the health.
I am confident on that one.
You committed hard, so let me show you exactly where it cracks.
Watch the arrow.
Reverse causation needs the effect, fewer sick days, to literally produce the cause,
opening the app.
But taking fewer sick days does not reach out and make someone download an app.
The two are not the same event, just both downstream of something else.
That awkwardness is your signal to drop reverse causation here.
The strongest attack is the third variable.
Picture the kind of person who downloads a wellness app the day it launches.
Health-conscious.
Already exercising, already sleeping well.
That same conscientiousness is what makes them take fewer sick days in the first
place.
So a common cause, the employee's existing health habits, drives both the app use
and the low sick days.
The app pockets credit it never earned.
And now I see it.
So the credited weaken answer might not even say the word cause.
It might just quietly mention that the early adopters were already the healthiest
employees on staff.
That is the trap inside the trap.
Weaken answers almost never announce themselves with causal vocabulary.
They slip in a new factor, or note a timeline, or describe a case where the cause
showed up and the effect never did.
You have to recognize the move, not wait for the word cause.
Now your turn, fresh argument.
Neighborhoods with more bookstores have higher average incomes,
so the argument concludes that opening bookstores raises a neighborhood's income.
Take a beat, run the checklist, and tell me which attack you reach for before you
solve it.
Alternative cause, the third-variable flavor.
Wealthier neighborhoods attract bookstores.
The wealth was already there and pulled the bookstores in.
So the bookstores did not create the income.
A common cause, the existing wealth, explains both the stores and the high incomes.
Now finish the self-explanation.
Say why that wrecks the argument instead of just nitpicking it.
Because it offers a complete rival story for the correlation that does not need the
author's arrow at all.
If the wealth is what draws the bookstores, then the bookstores-raise-income claim
loses its only support.
The two things still move together, but the author's explanation is no longer the
one doing the work.
And notice what you did not have to do.
You never proved bookstores can't affect income.
One plausible alternative is enough.
A weakener does not have to destroy the argument, it only has to make the causal
conclusion less likely.
Now flip the job.
If weakening pries doors open, strengthening shuts them.
Same three assumptions, opposite task.
Two moves to know.
Move one, rule out an alternative cause.
Move two, show the effect tracks the cause, present when the cause is present,
gone when the cause is gone.
Think of it as a control group, the cause switched on and switched off.
So strengthening is just closing the same doors that weakening opens.
That is the sentence to keep.
Back to the streetlights for the on-off test.
Imagine we learn that on the one block where a streetlight broke and stayed dark for
months, burglaries climbed right back up, and only on that block.
Cause present, effect there.
Cause absent, effect gone.
That is the presence-absence test doing real work, not just repeating that lights
and safety go together.
And to rule out the alternative cause, we would want something like,
the police patrols were exactly the same as the year before.
So patrols cannot be what explains the drop.
Now you are driving.
That eliminates the rival explanation and forces the credit back onto the lights.
Here is the classic strengthen trap, though, so pause and spot it before I do.
Which of these does nothing for the argument?
Option A, burglaries also dropped on three more streets that got new lights.
Option B, the streets with new lights and the streets without them had the same drop
in burglaries.
Option B does nothing, and worse than nothing.
If lit and unlit streets dropped the same amount, the lights are not tracking
anything, so that actually weakens it.
And A, more streets where lights and the drop appear together,
that is just repeating the correlation, which never proves the cause.
Both halves are exactly the trap the test sets.
Piling on more correlation is not strengthening.
A real strengthener has to rule out a rival or show the on-off pattern.
Repeating that X and Y go together just hands you the premise a second time and
calls it support.
Here is the whole episode crushed into one heuristic.
When you see X caused Y, attack the arrow three ways.
Is something else causing Y?
Is the arrow backwards?
Is it just a fluke?
Attack the arrow three ways.
That is the move, and it is the only sentence you have to carry out of here.
And to strengthen, I defend the arrow.
Rule out the something else, and show the effect switches on and off with the cause.
So back to the cold open.
The town swore the streetlights stopped the burglars.
But you can now see the patrols that went up the same year,
the statewide drop that lifted every town, the broken-light block that tells the
real story.
The author saw a correlation and drew an arrow.
Your job is never to draw it for them, it is to ask whether they earned it.
One warning I want to keep.
Don't diagram a causal claim like a conditional.
If I eat salty food I retain water sounds like an if-then, but a cause-and-effect
claim is a different system.
I attack it with the three arrows, not with sufficient and necessary.
Hold onto that, it saves people real points on test day.
Between now and next time, one tiny drill.
In any conversation today, catch one because someone says out loud,
then silently ask yourself, is that the cause, or just the thing that came before.
Train the reflex off the clock so it is automatic on the clock.
Next episode we stop attacking arguments and start building from them.
Inference questions.
What must be true versus what is most strongly supported, and why those two phrases
send you to two completely different answers.