For the fifth course, we will discuss machine learning and standard techniques used to get predictive models, and to assess accuracy of those models.

#### GLM (possibly constrained)

Classically, we use a penalized version of least squares (but this can be adapted to GLMs, when penalizing the negative log-likelihood). Because of Karush–Kuhn–Tucker conditions, having a constraint on the parameter is equivalent to the following penalized problem, when the constraint is on the \ell_2 norm of \boldsymbol{\beta},

We can also consider the \ell_1 norm of \boldsymbol{\beta},

Those two approaches can be see as a trade-off between accuracy (here the empirical risk on the left) and complexity of the model (on the right). And we can also consider a mixture of the two norms,

As we will see, it will also be possible to consider some penality related to fairness and discriminiation measures (in-processing).

#### Classifier and ROC Curves

We will also recall metrics used in the context of classification, such as the ROC curve

Each point of the curve can be related to two areas related to the distributions of the scores (in the two groups), for the same threshold – namely the false positive rate and true positive rate

Based on the ROC curve, we can define the AUC, the area under the ROC curve,

But for classifiers, the important challenge is to have calibrated scores, meaning that we want the score to be interpreted as the true underlying probability.

#### Calibration

Well-calibration is defined as follows

or (with different notations)

It is a well know properties in several applications.

The plot on the right is the calibration plot,

We can easily get that plot

This concept is related to the question “*do probabilities returned by some model represent reals probabilities ?*” For instance, below, we have pictures generated as some sort of geodesic between two pictures, with a woman on the top left, and a man in the bottom right, published in the New York Times. And below, “probabilities” given by https://www.picpurify.com/demo-face-gender-age.html.

We could agree that it is rather strange that probabilities (to have a man) do not increase continuously, but on top, with extremely high confidence, the model predicts that the picture is the one of a woman, and below, also with extremely high confidence, that the person is a man…

#### Data, observations vs. experiments

Then, after concept and notations related to models, we will talk about data. More specifically, the distinction between observations and experimentations.

Another popular classification is the one discussed by Judea Pearl.

So we will talk about association, correlation, causal inference, and counterfactuals.

#### “Correlated variables” or proxies

One important issue, is that with massive data, one can easily get a (good) proxy of almost any sensitive variable.

The concept is related to comonotonicity, or perfect correlation.

But this is clearly too strong, so we will discuss depedence measures, too.

#### Independence properties

Recall that independence is defined as follows

and we can consider a weaker form, based on null-covariance

or null-correlation

(sidenote, this correlation measure is bounded, and those bounds are related to Hardy-Littlewood inequality and optimal transport)

An interesting measure is the maximal correlation

or we can consider a weaker version, without consider all possible transformation, but only a subset

Another important concept is the one of conditional independence

(the later will be used in the context of causal graphs).

#### Causality

Before talking about causality, recall that what non-independence mean…

We can then construct causal graphs, or “directed acyclic graphs”

where nodes are the variables used in the model, and the outcome (usually that the end of the causal graph). Then we define paths

and the concept of *d*-separation

This concept is related to the statistical property of conditional independence

More precisely, we have the following Markov property on causal graphs

For example, for such a graphical model,

the joint distribution is \mathbb{P}[x_1,x_2,x_3,x_4]=\mathbb{P}[x_1]\times \mathbb{P}[x_2|x_1]\times \mathbb{P}[x_3|x_2]\times \mathbb{P}[x_4|x_3]and for the graphical model below

we have\mathbb{P}[x_1,x_2,x_3,x_4]=\mathbb{P}[x_1]\times \mathbb{P}[x_2]\times \mathbb{P}[x_3|x_1,x_2]\times \mathbb{P}[x_4|x_3]Those graphs can be related to structural models (with idiosyncratic noise denoted U), since

#### Potential outome

Another important concept is the concept of counterfactuals, and potential outome. In an ideal world, we would have observed the outome in both cases, with and without the treatement

but in real life, it’s only one of them,

And the goal will be, somehow, to estimate what the non-observed outcome would be. And then, classical quantites we wish to estimate are the average treatement effect, and the conditional version, based on some covariates.

This concept will be related to counterfactual fairness actually, when the “treatement” will be the sensitive attribute.

#### Twin network representation of the counterfactual

Finally, we will consider a so-called “twin network representation”. Consider a DAG, associated with some simple structural model

Based on a structural model, we can get values of idiosyncratic noise component

Then, we use those values on the twin representation, when the treatement is not 0, but 1. Counterfactuals are created by using the same noises

The difference between the two outcomes is the treatement effect, or the disparate treatement

or more generally, we write

This is an idea used in Plecko & Meinshausen, 2019, in the context of fairness, but we will discuss this more, later on…