Modeling Interference Using Experiment Roll-out

Experiments on online marketplaces and social networks suffer from interference, where the outcome of a unit is impacted by the treatment status of other units. We propose a framework for modeling interference using a ubiquitous deployment mechanism for experiments, staggered roll-out designs, which slowly increase the fraction of units exposed to the treatment to mitigate any unanticipated adverse side effects. Our main idea is to leverage the temporal variations in treatment assignments introduced by roll-outs to model the interference structure. We first present a set of model identification conditions under which the estimation of common estimands is possible and show how these conditions are aided by roll-out designs. Since there are often multiple competing models of interference in practice, we then develop a model selection method that evaluates models based on their ability to explain outcome variation observed along the roll-out. Through simulations, we show that our heuristic model selection method, Leave-One-Period-Out, outperforms other baselines. We conclude with a set of considerations, robustness checks, and potential limitations for practitioners wishing to use our framework

Causal Inference with Differentially Private (Clustered) Outcomes

Estimating causal effects from randomized experiments is only feasible if participants agree to reveal their potentially sensitive responses. Of the many ways of ensuring privacy, label differential privacy is a widely used measure of an algorithm's privacy guarantee, which might encourage participants to share responses without running the risk of de-anonymization. Many differentially private mechanisms inject noise into the original data-set to achieve this privacy guarantee, which increases the variance of most statistical estimators and makes the precise measurement of causal effects difficult: there exists a fundamental privacy-variance trade-off to performing causal analyses from differentially private data. With the aim of achieving lower variance for stronger privacy guarantees, we suggest a new differential privacy mechanism, "Cluster-DP", which leverages any given cluster structure of the data while still allowing for the estimation of causal effects. We show that, depending on an intuitive measure of cluster quality, we can improve the variance loss while maintaining our privacy guarantees. We compare its performance, theoretically and empirically, to that of its unclustered version and a more extreme uniform-prior version which does not use any of the original response distribution, both of which are special cases of the "Cluster-DP" algorithm.Comment: 41 pages, 10 figure

Causal Estimation of User Learning in Personalized Systems

In online platforms, the impact of a treatment on an observed outcome may change over time as 1) users learn about the intervention, and 2) the system personalization, such as individualized recommendations, change over time. We introduce a non-parametric causal model of user actions in a personalized system. We show that the Cookie-Cookie-Day (CCD) experiment, designed for the measurement of the user learning effect, is biased when there is personalization. We derive new experimental designs that intervene in the personalization system to generate the variation necessary to separately identify the causal effect mediated through user learning and personalization. Making parametric assumptions allows for the estimation of long-term causal effects based on medium-term experiments. In simulations, we show that our new designs successfully recover the dynamic causal effects of interest.Comment: EC 202

L'éducation des personnes diabétiques au Centre hospitalier de Niort (historique et mise en oeuvre)

POITIERS-BU Médecine pharmacie (861942103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

L'hyperparathyroïdie familiale isolée (à propos d'ne famille niortaise et de quatre familles nantaises)

POITIERS-BU Médecine pharmacie (861942103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Comparaison de trois méthodes d'étude du profil électrophorétique des immunoglobulines G dans le liquide céphalo-rachidien

La synthèse intrathécale des immunoglobulines décrite dans de nombreuses pathologies du système nerveux central se traduit biologiquement par l'apparition, dans le LCR seulement, d'un profil électrophorétique caractéristique sous forme de bandes oligoclonales. C'est un élément important du diagnostic biologique de la scélore en plaques. L'étude menée évalue les performances techniques et diagnostiques de trois coffrets commerciaux pour la détection d'un profil oligoclonal des IgC dans LCR de 129 patients atteints de diverses pathologies neurologiques. Les techniques sont toutes basées sur l'amplification de la révélation des bandes oligoclonales avec des antisérums marqués à la perosydase permettant l'utilisation de LCR non concentré après séparation des protéines par électrophorèse (Hydragel 6- CSF, Sébia) ou par sisoélectofocalisation (Hydragel 6-CSF Isofocusing, Sebia et Titan Gel IgG IEF, Helena). Le choix du laboratoire pour une application en routine s'oriente vers la technique d'isoélectrofocalisation de Sebia. Cettetechnique est de réalisation très simple et semi automatique. De plus elle présente une meilleure sensibilité que les deux autres méthodes, permettant plus souvent la mise en évidence de profils anormaux des IgG dans des échantillons ne présentant qu'un très petit nombre de bandes.BORDEAUX2-BU Santé (330632101) / SudocSudocFranceF