The Causality Checklist - Common Drivers

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來自 Northwestern University 的課程

Leadership through Marketing

個評分

Northwestern University

Leadership through Marketing

個評分

課程 4（共 6 門，Specialization 组织领导）

The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.

從本節課中

A Working Knowledge of Data Science with Florian Zettelmeyer

Get started with a Working Knowledge of Data Science by learning a simple framework that will help you catch a large fraction of the bad analytics you encounter on a regular basis.

Introduction7:18

Why Analytics Goes Bad3:34

The Data Generating Process4:50

Causality and Probabilistic Equivalence7:27

The Causality Checklist - Pre-Existing Differences4:45

The Causality Checklist - Common Drivers5:03

The Causality Checklist - Reverse Causality4:54

The Causality Checklist - Confounds and Summary5:57

Why Bad Causal Analytics Need Not Be Useless3:14

Conclusion to Course 4 with Dean Blount1:06

與講師見面

Greg Carpenter
James Farley/Booz Allen Hamilton Professor of Marketing Strategy, Director of the Center for Market Leadership, Faculty Director, Kellogg Markets and Customers Initiative (KMCI)
Kellogg School of Management
Florian Zettelmeyer
Nancy L. Ertle Professor of Marketing, Director, Program on Data Analytics at Kellogg
Kellogg School of Management
Sanjay Khosla
Adjunct Professor of Executive Education
Kellogg School of Management

[MUSIC]

The question,

are there any pre existing differences between groups can be very hard to answer.

That's why it's useful to have a few follow up questions.

This questions can help elicit why groups may not be probabilistically equivalent.

Here's the first one.

Is there a common driver of both group membership and outcome?

Or, are group membership and outcomes both responding to a common factor?

I know this sounds pretty abstract, but it'll become much clearer in a minute.

Okay, why don't we start with an example where we already know

that there are pre existing differences between groups.

How about the search engine advertising example.

Let's see whether this question can uncover

why the groups along the x axis are not probabilistically equivalent.

Can you come up with some factor that drives both which

ads people see and whether they end up buying a car.

Pause the video, take a minute to think about this, and then come back.

I asked you to come up with some factor that drives both.

Which ads people see and whether they end up buying a car.

The answer of course is they have pre existing interest in buying a car.

Those who are more interested in buying a car are more likely to type in a care

related search term, which in turn makes them more likely to receive

car ads and it also makes them more likely to end up purchasing a vehicle.

That is what we call a common driver of both group membership,

namely which ads you see, and outcome, namely whether you buy a car.

Now let's look back at the ultrasound example.

Can you come up with something that might drive both

which ultrasound machine is used and how long exams take.

Pause the video, take a minute to think about this and then, come back.

Let me propose one possible common driver namely, technician experience.

Here are ultrasound exam time data,

split up by the experience level of the technician.

Contrary to the initial exam time data we saw, it turns out that novice

technicians are nine minutes faster using the 2015 machines.

But they're not the only ones.

Experienced technicians are also faster, using the 2015 machines,

this time by six minutes.

If I were looking at these data I would think hm,

he hasn't really told me what fraction of technicians are novice experience.

Let's say its 50/50.

Then understand how one might get- 7.5 minutes.

If we reported the exam times are for all technicians together.

But how do we get from minus seven and

a half to the plus one we saw in the first chart?

The reason is that not all technicians care about ease of use.

The novice technicians, they love the new machines, 85% now use them.

But the most experienced technicians, they really know the 2008 machines and

they don't see much value in switching.

So 90% still use the old machines.

When you ask the analytics dashboard to compare the efficiency of the ultrasounds,

you are effectively pulling the measurement of exam time for

the 2015 machines from usage by novice technicians and

the measurement for the 2008 machines from the usage by experienced technicians.

You were not really measuring how the devices compare.

You were measuring how different technicians compare and

that is why you got the wrong answer.

Technician experience is a common driver.

Experience determines both which ultrasound a technician is likely to

use and how long exams take.

And because of that, the exams performed on the 2008 and

2015 ultrasound machines are not probabilistically equivalent.

Because the exams were performed by technicians with different levels

of experience.

That is why the causal statement that was implied by the original graph,

if I switch my technicians from the old to the new machines,

exam times would go up by a minute, is wrong and therefore, bad analytics.

So, is there a common driver of both group membership and

outcome is the first followup question when we have trouble

answering directly whether there are any pre existing differences between groups.

[MUSIC]

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