Category Archives: Seminar

Econometrics Seminars at Université de Montréal

This Thursday, I will present at the CIREQ Séminaire Marcel-Dagenais en Économétrie at Université de Montréal, ou paper Optimal Transport for Counterfactual Estimation: A Method for Causal Inference, written with Emmanuel Flachaire and Ewen Gallic.

Many problems ask a question that can be formulated as a causal question: “what would have happened if…?” For example, “would the person have had surgery if he or she had been Black?” To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).

Slides are available online. I will try to mention additional papers published this year, such as Fairness in Multi-Task Learning via Wasserstein Barycenters, Mitigating Discrimination in Insurance with Wasserstein Barycenters or more recently A Sequentially Fair Mechanism for Multiple Sensitive Attributes.

Présentation sur l’équité et la discrimination en assurance, pour Intact

Demain matin, avec Olivier et Marie-Pier Côté, on sera chez l’assureur Intact pour parler équité et discrimination. Olivier présentera ses travaux récents sur l’utilisation de modèles causaux pour proposer des modèles “équitables” en assurance. Le papier (a fair price to pay: exploiting directed acyclic graphs for fairness in insurance) sera bientôt disponible !

A Fair price to pay: exploiting directed acyclic graphs for fairness in insurance

Tonight (Montréal time), Marie-Pier Côté will give a talk on “a fair price to pay: exploiting directed acyclic graphs for fairness in insurance” based on recent joint work with our PhD student, Olivier Côté, in Melbourne, Australia

Many jurisdictions have laws or guidelines stipulating that insurance companies must not discriminate on some specified policyholder characteristics. Omission of the prohibited variables from the models removes direct discrimination, but does not prevent proxy discrimination — a phenomenon especially prevalent when powerful predictive algorithms are fed with an abundance of allowed covariates. In the actuarial literature, there remains some confusion on the definition of indirect discrimination: this impedes the understanding of the goals of each fairness methodology and their comparison. In the causal inference literature, many tools, such as directed acyclic graphs (DAGs), help uncover various types of biases. A DAG describes the causal relationships between variables of interest and has clear dependence implications. We exploit this tool for fairness to formally define direct and indirect discrimination, to discuss potential sources of bias, and to understand the properties of different fairness methodologies. Four families of fair scores (best-estimate, unaware, aware and corrective) are placed in the DAG representing the insurance pricing problem. This allows us to study their behaviour in terms of direct and indirect discrimination. A comprehensive pedagogical example illustrates our findings.

More to come soon…

Talk at the ESSEC Risk Seminar

Thursday, I will be at La Défense, in Paris (France), to give a talk at the ESSEC Risk Seminar, entitled Causal Inference and Counterfactuals with Optimal Transport, with Applications in Fairness and Discrimination. Slides are now available, and the talk will be based on same recent papers, starting with Mitigating Discrimination in Insurance with Wasserstein Barycenters (presented last week-end at BIAS 2023) but also Fairness in Multi-Task Learning via Wasserstein Barycenters, and A Sequentially Fair Mechanism for Multiple Sensitive Attributes. There is also the textbook that should appear before the winter.

Talk at SINCLAIR (Saclay INdustrial Collaborative Laboratory for Artificial Intelligence Research)

This afternoon, I will give a talk at SINCLAIR (Saclay INdustrial Collaborative Laboratory for Artificial Intelligence Research), invited by Nicolas Bousquet, on causal inference and counterfactuals with optimal transport. It will be based on our paper Optimal Transport for Counterfactual Estimation: A Method for Causal Inference. Slides are online as well as a nice appendix with R codes.

Optimal Transport for Counterfactual Estimation: A Method for Causal Inference

For those who wish to reproduce the techniques proposed in our paper, Optimal Transport for Counterfactual Estimation: A Method for Causal Inference, Ewen Gallic has put online some nice pages, with the application mentioned in the paper (both univariate and bivariate, including confidence intervals with bootstrap), as well as simpler examples, which I use in the slides, to present the method

http://egallic.fr/Recherche/Transport_Counterfactual/

I will present this work at the Bachelier Seminar, in Paris, at the end of the week. Slides are online here.

Many problems ask a question that can be formulated as a causal question: “what would have happened if…?” For example, “would the person have had surgery if he or she had been Black?” To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).

Talk at StatQAM on Counterfactuals and Optimal Transport

Next Thursday, I will present our recent work at the StatQAM seminar, with Emmanuel Flachaire ajd Ewen Gallic, on Optimal Transport for Counterfactual Estimation: A Method for Causal Inference

Many problems ask a question that can be formulated as a causal question: “what would have happened if…?” For example, “would the person have had surgery if he or she had been Black?” To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).

Slides are now online.

Talk on fairness and “differential pricing” in insurance

Tomorrow morning, I will give a talk on fairness, “differential pricing” and “price walking” in insurance, at the AXA Chief Actuary Meeting. Slides are available online.

I will get back on two reports published recently (that can be related to the recent EIOPA consultation)

The first one was the report by the Sweedish Finans Inspektionen, Fär lojala försäkringstagare betala mer?

and the study by the Central Bank of Ireland.

They get back on price walking, seen as a discrimination problem,

It found that pricing practices applied by insurance providers could result in unfair outcomes for some consumers in the private car and home insurance markets. These pricing practices include “price walking”, where consumers are charged higher premiums, relative to the expected cost, the longer they remain with an insurance provider. The Report indicates that the CBI considers this practice to be unfair, a point emphasised in the press release accompanying the report. The Report found that long term customers who stayed with the same insurer for 9 years or more, paid on average 14% more on private car insurance and 32% more on home insurance than the equivalent customer renewing for the first time.

They provide interesting graphs, with prices of motor insurance

as well as household insurance,