This Tuesday, I will give a talk on fairness at the Akur8 ratemaking seminar. Slides are available online.
Category Archives: Seminar
Value-at-risk Forecasting via Sieves
Friday (and Saturday), the 2023 NBER-NSF conference on time series will take place at UQAM. Philipp Ratz will present some recent work on Value-at-risk Forecasting via Sieves.
A previous version of the paper is available on ArXiv.
Le défi de l’assurabilité des risques climatiques
Mercredi soir, je participerai à un évènement organisé par Data Driven Montréal, sur le thème du défi de l’assurabilité des risques climatiques. J’ai préparé quelques slides pour lancer la discussion.
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.
Discussion à l’ACPR
Mardi, je serais à la Banque de France, pour discuter le papier d’Eric Vansteenberghe, intitulé “Insurance Supervision under Climate Change: A Pioneers Detection Method“.
Talk at the seminar at the Bayes Business School (City University, London, UK)
Tomorrow afternoon, I will give a talk at the Bayes Business School (City University, London, UK), on Fairness and Discrimination in Actuarial Predictive Models using Optimal Transport. Slides are now online.
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.
ASTIN Reading Club
This Tuesday morning (7 am), I will give a talk at the ASTIN reading club, on auto-calibration. More precisely, we will look at the topic of ensuring the calibration of machine learning models for non-life pricing. Slides are available here.
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,
Talk on Bayesian models in actuarial science
Risque de sécheresse et de subsidence
Jeudi, en arrivant sur Paris, je ferai une intervention pour présenter predicting drought and subsidence risks in France, publié dans le numéro spécial Drought vulnerability, risk, and impact assessments: bridging… de NHESS (Nat. Hazards Earth Syst. Sci.), écrit avec Molly James, et Hani Ali, ainsi que le travail sur les inondations, en France, Insurance against natural catastrophes: balancing actuarial fairness and social solidarity, lors d’une discussion avec Marc Bagarry
Les slides sont en ligne.
Genre, algorithmes et droit
En ce jour de fête nationale, je vais participer ce matin à une discussion regards croisés dans le champ des assurances et de la responsabilité civile, avec Rodolphe Bigot, dans une journée Genre, algorithmes et droit, organisée à Aix-Marseille par Sophie Sereno.
Journée d’étude “Genre, Algorithmes et Droit” à Aix-en-Provence
Vendredi, je participerais avec Rodolphe Bigot à la journée d’étude “Genre, Algorithmes et Droit” à Aix-en-Provence.
Présentation au Réseau de recherche en sécurité routière du Québec
Mardi, je donnerais un exposé sur le thème “apport des données télématiques pour comprendre le risque en assurance automobile” aux membre du réseau de recherche en sécurité routière du Québec. Les slides sont en ligne.