This course seeks to turn learners into informed consumers of social science research. It introduces concepts, standards, and principles of social science research to the interested non-expert. Learners who complete the course will be able to assess evidence and critically evaluate claims about important social phenomena. It reviews the origins and development of social science, describes the process of discovery in contemporary social science research, and explains how contemporary social science differs from apparently related fields. It describes the goals, basic paradigms, and methodologies of the major social science disciplines. It offers an overview of the major questions that are the focus of much contemporary social science research, overall and for China. Special emphasis is given to explaining the challenges that social scientists face in drawing conclusions about cause and effect from their studies, and offers an overview of the approaches that are used to overcome these challenges. Explanation is non-technical and does not involve mathematics. Statistics and quantitative methods are not covered. Explore the big questions in social science and learn how you can be a critical, informed consumer of social science research. Course Overview video: https://youtu.be/QuMOAlwhpvU After you complete Part 1, enroll in Part 2 to learn how to be a PRODUCER of Social science research. Part 2: https://www.coursera.org/learn/social-science-research-chinese-society
7.7 Matching approaches
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Do curso por The Hong Kong University of Science and Technology
Social Science Approaches to the Study of Chinese Society Part 1
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The Hong Kong University of Science and Technology
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Cause and Effect
In Week 7, we will focus on Cause and Effect. By the end of this week, you should understand the basic approaches that social scientists follow in trying to establish that an observed relationship reflects cause and effect.
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Cameron CampbellProfessor of Social Science
Associate Dean of the School of Humanities and Social Science
Hi, in this module, we'll talk about Matching approaches.
In experiments, matching is sometimes used as part of a randomized
assignment to control and treatment.
Pairs of subjects identical on the key variables that are used for
the matching are then randomly divided into control and treatment.
So for example, if we have a group of subjects we might pick out
pairs of subject that have similar ages or perhaps are the same sex.
Or similar on other characteristics and
then randomly assign one of them to control and then one of them to treatment.
This reduces the probability that chance variation in the distribution of
the absorbed variables could be two differences between control and treatment.
That is chance variation in the observable variables that were the basis for
the matching.
So for example, if we matched our subjects based on age group,
sex, and marital status, we would ensure that the control and
treatment groups had identical numbers of people in terms of
the distribution of age, group, sex, and marital status.
And the differences between the control and
treatment groups in terms of the composition on those variables, would
not drive any observed differences between the control and treatment outcomes.
Control variables may be further introduced in analysis of the results to
achieve the same effect.
But may not be as effective.
So, if we think about a study design with a matching.
Here we have people who come in different shapes and different colors.
Red and blue, square and circle.
So the idea of matching is that if we're dividing into control and
treatment we pick pairs of subjects who are identical on,
in this case, their shape and their color and
then, randomly divide the members of each pair into control and treatment.
So here, we have two blue squares divided.
And we continue doing this until we run out of subjects.
The result is that we have control and treatment groups that are identical in
terms of the numbers of red and blue, and the numbers of squares and circles.
So that any differences between them can't be the result of differences in
the proportions of the subjects that are squares or circles, or red and blue.
If we don't have matching and we just pick people at random,
it's always possible that the control and treatment groups may differ
in terms of their distribution of some of these observables.
So, for example, may have different numbers of reds and
blues, and squares and circles.
Now if these characteristics have nothing to do with the outcome of interest,
then we're okay.
But actually, if they influence the outcome of interest,
then we could end up with a problem for the interpretation of our results.
Now for observational studies, we can actually conduct matching.
There's one approach, propensity score matching,
which seeks to identify treatment effects in observational data.
Subjects are matched according to their propensity to experience the treatment.
So for example, important studies of the effects of college
education attempt to match people in terms of their likelihood
of going to college based on their other characteristics.
But who differ in terms of whether or not they actually went to college.
So if we think of going to college as the treatment and
we take pairs of people with identical chances of going to college at
least based on the other observables and you differ only in terms of whether or
not they actually went, that's like a difference between control and treatment.
So, the propensity that is used as the basis of
the matching is predicted from the results of a regression
of whether the subject experienced the treatment on the other variables.
So in the example I just gave, the chances that a person went to
college are regressed on a wide variety of family background characteristics.
And other things that we normally expect to affect the chances of going on to
college.
Then outcomes are compared for subjects who had similar propensities.
In this case, similar chances of going to college would actually differ in terms
of whether or not they experience the treatment and
it is whether or not they actually did go to college.
So among people who are equally likely to go to college based on
their background characteristics but who differed in terms of whether or
not they went, then we look at differences in their outcomes.
For example,
differences in their incomes or differences in other outcomes of interest.
And then claim that because these people were matched in terms of their chances of
going to college, the differences in their outcomes are the result
of the differences between them in terms of whether they actually went to college.
So for estimating the propensity,
people typically will look at some outcome variable Y and think of it as a 0 or
1 variable according to whether or not the subject experiences the treatment.
So, Y here might be 0 or 1 according to whether somebody goes to college.
And then, X are various variables that influence the chances of going to college.
And then we assumed that Y is Independent of unobserved variables.
There's no W's working around there that might also be influencing Y.
This is called the ignorability assumption.
And then observations are matched on estimated propensities of Y based on X,
and their outcomes compared.
So we get the propensities.
By essentially once we have run the regression,
predicting the probability of Y being 1 or 0 for every single observation.
And then again, we match observations that have similar chances of experiencing Y.
So here's an example where we've got some subjects who have different propensities
of experiencing a treatment, perhaps different chances of going to college.
So as much as possible, we put them into pairs where they are similar in terms of
their chances of going to college.
Now we can't make the match exact but
roughly speaking we have now four pairs of people where they're
different in terms of whether or not they actually went to college.
But they have similar chances of actually having gone to college as predicted from
their background characteristics.
Now if we're looking at the effects of college attendance on earnings.
So the basic idea is that we first look at the effects of background
characteristic on the chances of going to college to then estimate our
propensity to go to college as predicted by the background characteristic.
And then we divide people according to their propensity to attend college,
that is, the probability that they would go to college.
And then compare their earnings according to whether they attended college and or
didn't attend.
And in this example, based on an actual study that was done by Jennie Brand and
Yu Xie, we compare the earnings of people that
attended college and the people who didn't.
And in this particular example, which is similar to the published one,
the effects of going to college were strongest for
the people who were, actually, least likely to attend college.
That the gap between the earnings of the people who attended college and
didn't attend college were largest for
the people who have the lowest chances of attending college.
So this is a example of a approach to matching,
in this case it's called propensity square matching.
It's sometimes used for observational data.
Again, trying to match people in terms of their probability
of experiencing the treatment of interest.
In this case, attending college.
And then comparing them on some outcome according to whether or
not they actually did experience the treatment.
In this case again, going to college.
It's becoming extremely popular but there are numerous critiques.
One of the most important is whether or
not the ignorability assumption as mentioned earlier is really valid.
Whether the observed characteristics that are used to predict,
obtaining the treatment and therefore calculate the propensity
really capture all of the differences of interest and
that unobserved variables are not affecting the outcome.
That's one critique and there also critiques about, very technical ones,
about some of the issues that are related to the actual matching
on the propensities.
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