Les espaces de l'halieutique

Trois espèces de thons constituent la ressource cible des pêcheries palangrières en Polynésie française : le germon (#Thunnus alalunga), le thon à nageoires jaunes (#Thunnus albacares) et le thon obèse (#Thunnus obesus$). Les distributions géographique et bathymétrique de cette ressource sont étroitement liées aux paramètres hydrologiques et trophiques de l'environnement. Dans le cadre du programme Ecotap, des pêches expérimentales réalisées à l'aide d'une palangre instrumentée ont été couplées à des relevés hydrologiques et des prospections acoustiques. La structure hydrologique est étudiée à l'aide de profils de température, salinité et d'oxygène dissous jusqu'à 500 mètres de profondeur. Une analyse typologique des stations menée à partir de ces variables permet de définir trois zones hydrologiques. Les écho-prospections réalisées entre 0 et 500 mètres, à l'aide d'un sondeur Simrad EK 500 permettent de mesurer une réponse acoustique considérée comme représentative de la biomasse en poissons micronectoniques. Cette biomasse est considérée comme un indice de la capacité trophique du milieu pour les thons. L'étude de la distribution verticale et horizontale du micronecton permet de définir trois zones assez similaires aux zones hydrologiques quant à leur distribution spatiale. La distribution des thons est étudiée à partir des captures réalisées à la palangre instrumentée ainsi qu'à l'aide des échos individualisés détectés par acoustique pouvant être considérés comme étant des thonidés. Dans les deux cas, des informations spatio-temporelles précises sont disponibles pour chaque poisson. Les distributions horizontale et verticale des thons sont étudiées en fonction du volume d'habitat et de la capacité trophqiue du milieu. La stratégie d'occupation de l'espace apparaît différente selon les espèces... (D'après résumé d'auteur

Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?

There are many measures to report so-called treatment or causal effect: absolute difference, ratio, odds ratio, number needed to treat, and so on. The choice of a measure, e.g. absolute versus relative, is often debated because it leads to different appreciations of the same phenomenon; but it also implies different heterogeneity of treatment effect. In addition some measures but not all have appealing properties such as collapsibility, matching the intuition of a population summary. We review common measures, and their pros and cons typically brought forward. Doing so, we clarify notions of collapsibility and treatment effect heterogeneity, unifying different existing definitions. But our main contribution is to propose to reverse the thinking: rather than starting from the measure, we propose to start from a non-parametric generative model of the outcome. Depending on the nature of the outcome, some causal measures disentangle treatment modulations from baseline risk. Therefore, our analysis outlines an understanding what heterogeneity and homogeneity of treatment effect mean, not through the lens of the measure, but through the lens of the covariates. Our goal is the generalization of causal measures. We show that different sets of covariates are needed to generalize a effect to a different target population depending on (i) the causal measure of interest, (ii) the nature of the outcome, and (iii) a conditional outcome model or local effects are used to generalize

Reweighting the RCT for generalization: finite sample error and variable selection

Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain characteristics compared to the target population, for which one wants conclusions on treatment effectiveness. Re-weighting trial individuals to match the target population can improve the treatment effect estimation. In this work, we establish the exact expressions of the bias and variance of such reweighting procedures -- also called Inverse Propensity of Sampling Weighting (IPSW) -- in presence of categorical covariates for any sample size. Such results allow us to compare the theoretical performance of different versions of IPSW estimates. Besides, our results show how the performance (bias, variance, and quadratic risk) of IPSW estimates depends on the two sample sizes (RCT and target population). A by-product of our work is the proof of consistency of IPSW estimates. Results also reveal that IPSW performances are improved when the trial probability to be treated is estimated (rather than using its oracle counterpart). In addition, we study choice of variables: how including covariates that are not necessary for identifiability of the causal effect may impact the asymptotic variance. Including covariates that are shifted between the two samples but not treatment effect modifiers increases the variance while non-shifted but treatment effect modifiers do not. We illustrate all the takeaways in a didactic example, and on a semi-synthetic simulation inspired from critical care medicine