Welcome to this session on

Causality and Machine Learning

as a part of Frontiers in

Machine Learning.

I'm Amit Sharma from Microsoft

Research and your host.

Now of course I presume you

would all agree that

distinguishing correlations from

causation is important.

Even at Microsoft, for example,

when we're deciding which

product feature to ship or when

we're making business decisions

about marketing, causality is

important.

But in recent years, what we're

also finding is that causality

is important for building

predictive machine learning

models as well.

So especially if you're

interested in out-of-domain

generalization having your

models not brittle, you need

causal reasoning to make them

robust. And in fact there are

interesting results even about

adverse robustness and privacy

where causality may play a role.

This is an interesting time at

the intersection of causality

and machine learning. And we

now have a group at Microsoft as

well that is looking at these

connections.

I'll post a link in the chat.

But for now, today I thought we

can ask, all ask this question

what are the big ideas that will

drive further this conversation

between causality and ML.

And I'm glad that today we have

three really exciting talks.

Our first talk is from Susan

Athey, economics of technology

professor from Stanford. She'll

talk about the challenges and

solutions for decision-making

under high dimensional and how

generative data modeling can

help.

And in fact when I started in

causality, Susan's work was one

of the first I saw that was

making connections between

causality and machine learning.

I'm looking forward to her talk.

And next we'll have Elias

Bareinboim, who will be talking

about the three kinds of

questions we typically want to

ask about data and how two of

them turn out to be causal and

they're much harder.

And he'll also talk about an

interesting emerging new field,

causal reinforcement learning.

And then finally we'll have

Cheng Zhang from Microsoft

Research Cambridge.

She'll talk about essentially

give a recipe for how to build

models, neural networks that are

robust to adversal attacks. And

by now you've guessed in the

session she'll use causal

reasoning. And at the end we'll

have 20 minutes for open

discussion. All the speakers

will be live for your questions.

Before we start, let me tell you

one quick secret.

All these talks are prerecorded.

So if you have any questions

during the talk, feel free to

just ask those questions on the

hub chat itself and our speakers

are available to engage with you

on the chat even while the talk

is going on.

With that, I'd like to hand it

over to Susan.

SUSAN ATHEY: Thanks so much for

having me here today in this

really interesting session on

machine learning and causal

inference.

Today I'm going to talk about

the application of machine

learning to the problem of

consumer choice.

And I'm going to talk about some

results from a couple of papers

I've been working on that

analyze how firms can use

machine learning to do

counterfactual inference for

questions like how should I

change prices or how should I

target coupons.

And I'll also talk a little bit

about the value of different

types of data for solving that

problem.

Doing counterfactual inferences

is substantially harder than

prediction.

There can be many data

situations where it's actually

impossible to estimate

counterfactual quantities.

It's essential to have the

availability of experimental or

quasi experimental variation in

the data to separate correlation

from causal effects.

That is, we need to see whatever

treatment it is we're studying,

that needs to vary for reasons

that are unrelated to other

unobservables in the model. We

need the treatment assignment to

be as good as random after

adjusting for other observables.

We also need to customize

machine learning optimization

for estimating causal effects

and counterfactual of interest

instead of for prediction.

And indeed, model selection and

regularization need to be quite

different if the goal is to get

valid causal estimates. That's

been a focus of research,

including a lot of research I've

done.

A second big problem in

estimating causal effects is

statistical power. In general,

historical observational data

may not be informative about

causal effects. If we're trying

to understand what's the impact

of changing prices, if prices

always change in the past in

response to demand shocks, then

we're not going to be able to

learn what would happen if I

change the price at a time when

there wasn't demand shock. I

won't have data from that in the

past.

I'll need to run an experiment

or I'm going to need to focus on

just a few price changes or use

statistical techniques that

focus my estimation on a small

part of the variation of the

data.

Any of those things is going to

lead to a situation where I

don't have as much statistical

power as I would like.

Another problem is effect sizes

are often small.

Firms are usually already

optimizing pretty well.

It will be surprising if making