- [Instructor] In "The Book of Why,"
Pearl acknowledges the value of control variables,
like the covariate we used in INCOVA.
But there's a bit of caution as well.
They aren't a perfect solution.
We'll get to the newer options
in the second half of the course,
but let's first summarize his major concerns.
In the book, they put it this way.
"Oddly, statisticians both over and underrate
"the importance of adjusting for possible confounders,"
which is what you're trying to do
with the control variables.
"They overrate it in the sense that they often control
"for more variables than they need to
"and even for variables they should not control for.
"At the same time,
"statisticians greatly underrate controlling in the sense
"that they are loath to talk about causality at all,
"even if controlling has been done correctly."
They give a powerful example of this.
They discuss a study that had the potential
to show reduced death rates among retired men in Hawaii
in the category the researchers called intense walkers.
They quote the study in the book,
which according to their assessment,
controlled for every reasonable factor.
Factors like age, physical condition and several others,
yet when the study was done,
even though the numbers were quite convincing
and the statistical controls were thorough,
the researchers were reluctant to conclude
that the walking got the credit.
This discussion isn't just about
the technique of control variables.
Yes, control variables are powerful
and we need to learn some new techniques too,
like those discussed in the final section of the course,
but we also need a different mindset.
We have to overcome this reluctance to discuss causality
even when the data gives us ample evidence to do so.
And what's also trying not to be overly concerned
about the fact that in data science,
we often find ourselves in a position
where it is a poor fit to imitate controlled experiments
like double blind studies
or like the techniques that Fisher innovated at Rothamsted.
Those techniques simply don't fit every situation
so our professional development requires us
to be familiar with how experiments work
and how to use tricks, like statistical controls,
but they are just some of the tools in the toolkit.
In the second half of the course,
we'll be exposed to some of the newer options
in the study and practice of causal inference.