Category Archives: Seminar

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,

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.