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Welcome back.We're still in week six.This is lecture two of number five.In this section, we're going to talkabout this idea of counterfactuals, acritically important idea forunderstanding causal inference in socialepidemiology.I want to point out that there's lots ofmath that one can do.Algebra, if you wish, of counterfactualthinking.I'm not going to go into that in thiscourse.If you want to Google it, you can look atsome of the citations and some of theliterature for reading for this course,but here I want to be as straightforwardand simple as possible.Don't forget about the reading by theway.So first some history.Where does this idea of counterfactual

come from?Well, it comes, most people say from theearly work of the philosopher David Hume.He was writing in the 1700s, and Hume wasworking on the problem of scientificinduction.How do we know anything causes somethingin the world?So, and relatedly, how do we predictthings in the world?So the sun rising yesterday and the daybefore, does it tell us anything aboutthe sun rising tomorrow?

Hume said no.In other parts of his work, while workingon the very same problem, he talked aboutthe key element of causation being theso-called but for condition.But for the virus, you would not havegotten the chest cold.But for the car accident, you would nothave been injured.But for this course, you wouldn't learn alot about social epidemiology.The idea of but for was really notaddressed for a couple of hundred years,

but it was picked up again by thephilosopher David Lewis, and published inhis important 1973 text calledcounterfactuals.Now this is an intense text of alogician, someone who works on logic.And you can read it if you wish.It's very difficult.But the first line of this important textalways struck me interesting.

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And he writes, if kangaroos had no tails,would they topple over?That is, but for the tail, would thekangaroo topple?And from there he goes on to lots ofmathematics and other things.But here is the philosopher Lewis sayinghey, counterfactuals are important,here's how to think about them.These ideas were picked up and advancedin a stastis-, statistical framework, ina research framework, by the statisticianwho was at Harvard, Don Rubin.And Rubin advanced these ideas and talkedabout it, one of the first people inmodern statistics to address causationand causality.From here, Rubin talked about it in termsof potential outcomes.It's the same idea as counterfactuals, atleast for purposes here.Sometimes people call this the Rubin'smodel.So there's lots of jargon, but it's

really the same idea.Recent work, particularly in socialepidemiology, is addressing what's calledthe closest possible world assumption.That is, how much of a different worldcan you imagine?Is it worth imagining a world withkangaroos and no tails?Or is that just philosophicalnavel-gazing?So when we talk about comparing things,or looking at a world in a different way,how far afield do we want to go?

Is it worth, scientifically, imagining aworld where everyone loved each other andtook care of one another?That's kind of a far place to go, and notvery close to the world we live in.It might be better to have a closer worldimagined, where we imagine differentpolicies.Obama-care, or the affordable care act,or something like that changing, whichseems quite realistic.And then think about its impact on ourhealth outcomes.

Well let me try to offer some pictorialor illustrative demonstration ofcounterfactuals.The first point is to imagine a person.Here is just a black, simple stickfigure.And this person is exposed to McDonald's,eats Mcdonald's And after some period oftime, this person gets a little thicker.His body mass index might have gone up.

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The important point about counterfactualsis we take that very same person, a blackstick figure.At the very same time, we roll back theuniversal clock.And we imagine him in a world withoutMcDonald's, and then we see what his BMIor body mass index was with that worldwithout or but for McDonald's.And we see here in the imagined world,he's thinner.So is it, this suggests that in a worldwith McDonald's, stick figure gets thick.In a world without McDonald's, StickFigure stays thin.The causal effect of McDonald's, sinceeverything else is the same, is thedifference in the body mass index fromthese two diagrams.So what we end up doing when we do causalinference is compare the stick figurewith McDonald's, thicker to the stickfigure without McDonald's, thinner.And if the difference in BMI is 10 units,

then we say the cause McDonald's on BMIis 10 units.Of course, it's not possible, it is notpossible to ima-, to observe, to see theworld with black stick figure, withMcDonald's.And black stick figure at the same time,and the same place, without McDonald's.That's why we say counter to fact, orcounter-factual.Only one of those scenarios is true.Black stick figure had McDonald's ordidn't.

What we do in the real world, where wecan observe things, is find anotherperson, in this case, blue stick figure.And what we want to do is compare theworld with the black stick figure and theworld with the outcome of the blue stickfigure And one is exposed to McDonald's.One is not.The black stick figure is heavy, the bluestick figure is not.So now we're at the critical junction.Is McDonald's causing the change inobesity?

The answer is yes only if the black andblue stick figures are exchangeable orotherwise identical.If black and blue are flip flappable,exchangeable, identical, you pick theword, then we can say, yes McDonald's iscausing the change in obesity.But, if say the blue stick figureexercises differently or has differentmetabolism, then there's another

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explanation for the observed change inobesity.And that is a competing explanation, whatepidemiologists call confounding.So push come to shove, this game ofcausal inference is all aboutcomparisons.What we observe is, say, a black stickfigure under one scenario.What we'd like is the same black stickfigure in an alternative scenario, can'tobserve that.That's the counterfactual.So we seek a blue stick figure tosubstitute for the unobservable blackstick figure.And to the extent that the blue and blackcounterfactual are exchangeable, we candraw causal conclusions, to the extentthey are not exchangeable, or comparable,or flip flappable.We have confounding, or other sources ofbias, and that's the rub of epidemiologicresearch, causal inference.

When done right, finds the bestcounterfactual substitute for theunobservable counterfactual scenario.We see comparison groups or person whoare like our treated or exposed groups inevery way except for the exposure.The virus, the McDonald's, the policy.The best substitutes, as I've mentioned,are exchangeable.Lack of exchangeability is whatepidemiologists calls confounding.How do we do this?How do we achieve exchangeability in the

real world?The best way is through randomization.We flip a coin.Some people are treated or exposed,others are not.On average, in the long run, those twogroups are statistically equivalent.That's why randomization is so important.That's why the work of Ronald Fisher isso important.In the end, it's all about the comparisongroup.Epidemiologists should always ask,

"compared to what"?The virus had this effect compared towhat?The policy had that effect compared towhat?Of course, there are some limitations ofcounterfactuals.Just a few, and I know this is a busyslide.The whole approach of counterfactuals

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tends to focus our attention on a singlecause.But we all know that most health outcomesare from multiple causes.Counterfactuals also tend to focus on themanipulation of things, which leaves thequestion of a gender or a race effectquestionable because we cannot changesomeone's race or gender Counterfactualsoften prevent us from thinking aboutlevel of exposure.It's either exposed or unexposed.What about degrees of exposure, more of amedicine, more of the carcinogen or less.Counterfactuals are less prone to help uswith that kind of problem Counterfactualsoften don't illuminate the mechanism bywhich the change occurs.So, we know that McDonald's is causingobesity, but we don't know how exactlythat happens.Counterfactuals don't fit very well withthe idea of necessary and sufficientconditions, which has, for a long time,

been part of the literature or thinkingon causal effects.So that's a little troubling.Counterfactuals don't help us with effectheterogeneity.That is, the effect of the virus on mehas this outcome.The effect of the virus on you has thatoutcome on someone else, yet anotheroutcome.So it's still having an effect, but thoseeffects vary.That's effect heterogeneity,

counterfactuals struggle with that kindof question.It's not impossible, it's just moreadvanced work and thinking.Counterfactuals are useless at theindividual level.With the data alone, I can't tell whetherthe virus Being brought to me is causingmy cold, or whether there was somethingelse.But at a population level, we say yes,the virus in the group creates anincrease of 40% of colds or some other

condition.Counterfactual, counterfactual thinkingworks at the group level, at least that'swhat we can observe.Finally, counterfactuals aren't very goodfor an important area of socialepidemiology and that has to do withdynamics in groups and feedback loops.What do I mean?Well, does socioeconomic status cause bad

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health, or does bad health cause lowersocioeconomic status?The answer is both, because there's acircular, or feedback loop phenomenon.Counterfactuals aren't very helpfulthere.Parting thoughts when we think aboutcausal inference.We always bring to our data some model,some theory of causation.It's not in the data itself so it'simportant to remember that.And as a result we have to be cautiousbecause we need to self reflect on ourown cognitive framework, our ownscientific self scrutiny.The fact is we're bombarded by data, allkinds of stimuli, every moment of everyday.We filter that out.Scientists filter those things out inuseful ways.That's the key.