Concept

Path analysis

Publications associées (13)

One significant and simultaneously interesting problem in urban mobility has to do with the study of shared spaces where various categories of users coexist and act together. This paper aims to examine the behavior and preferences of pedestrians and cyclis ...

2023

A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

Negar Kiyavash, Ehsan Mokhtarian, Saber Salehkaleybar

We study experiment design for unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skele ...

2022

Separable Effects for Causal Inference in the Presence of Competing Events

Mats Julius Stensrud

In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatm ...

2020

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

Negar Kiyavash, Saber Salehkaleybar, Kun Zhang

We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, the inferred causal relationships among the observed ...

MICROTOME PUBL2020

Development of Advanced Methodologies for Monitoring and Modelling of Neutron Noise in Modern LWR Cores

Dionysios Chionis

Nuclear reactors are inherently stochastic systems, in which neutronic and thermal-hydraulic parameters fluctuate continuously even during steady-state conditions. In addition, structural components vibrate due to the coolant hydraulic forces. This stochas ...

EPFL2020

Effect heterogeneity and variable selection for standardizing causal effects to a target population

Mats Julius Stensrud, Anders Huitfeldt

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing resul ...

2019

Extrapolating Paths with Graph Neural Networks

Andreas Loukas, Jean-Baptiste Francis Marie Juliette Cordonnier

We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. In particular, we focus on natural paths occurring as a by-product of the int ...

IJCAI, Inc.2019

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Willy Zwaenepoel, Diego Didona, Kristina Spirovska

In this paper we present a new approach to implementing causal consistency in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). The optimism in our approach lies in that updates from a remote data center are immediately made vi ...

Ieee Computer Soc2017

A method of probabilistic map distribution of path likelihood

Michel Bierlaire, Jingmin Chen, Jeffrey Newman

A method is proposed to robabilistically map location observations to the underlying network. Instead of generating a single path as the map matching algorithms do, this method aims at calculating a likelihood for each potentially true path to have been th ...

2009

Generating probabilistic path observation from GPS data for route choice modeling

Michel Bierlaire, Jingmin Chen, Jeffrey Newman

Map matching algorithms are the conventional way to generate path observations from GPS data for route choice models. The deterministic matching may introduce extra biases to parameters of route choice models if the matching is wrong. In this paper, a new ...

2009

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