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Related publications (25)

Modulation of Visually Induced Self-motion Illusions by α Transcranial Electric Stimulation over the Superior Parietal Cortex

The growing popularity of virtual reality systems has led to a renewed interest in understanding the neurophysiological correlates of the illusion of self-motion (vection), a phenomenon that can be both intentionally induced or avoided in such systems, dep ...

Mit Press2024

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects

Mats Julius Stensrud

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus, and codeine has no effect on pain in individuals with certain polym ...

Lippincott Williams & Wilkins2024

Content Moderation in Online Platforms

Manoel Horta Ribeiro

A critical role of online platforms like Facebook, Wikipedia, YouTube, Amazon, Doordash, and Tinder is to moderate content. Interventions like banning users or deleting comments are carried out thousands of times daily and can potentially improve our onlin ...

EPFL2024

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Elena Beanato, Esra Neufeld, Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Traian Popa, Pierre Theopistos Vassiliadis, Julie Duqué, Fabienne Windel

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

Nature Portfolio2024

Deplatforming did not decrease Parler users' activity on fringe social media

Robert West, Manoel Horta Ribeiro

Online platforms have banned ("deplatformed") influencers, communities, and even entire websites to reduce content deemed harmful. Deplatformed users often migrate to alternative platforms, which raises concerns about the effectiveness of deplatforming. He ...

OXFORD UNIV PRESS2023

The arrow-of-time in neuroimaging time series identifies causal triggers of brain function

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Enrico Amico, Maria Giulia Preti, Raphaël Pierre Liégeois

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping phy ...

Wiley2023

Causal inference with recurrent and competing events

Matias Janvin, Mats Julius Stensrud, Pal Christie Ryalen

Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in ath ...

SPRINGER2023

CDKL5 sculpts functional callosal connectivity to promote cognitive flexibility

Valerio Zerbi

Functional and structural connectivity alterations in short- and long-range projections have been reported across neurodevelopmental disorders (NDD). Interhemispheric callosal projection neurons (CPN) represent one of the major long-range projections in th ...

SPRINGERNATURE2023

Longitudinal incremental propensity score interventions for limited resource settings

Mats Julius Stensrud, Aaron Leor Sarvet

Many real‐life treatments are of limited supply and cannot be provided to all individuals in the population. For example, patients on the liver transplant waiting list usually cannot be assigned a liver transplant immediately at the time they reach highest ...

2023

Interventionist estimands in event history analysis

Matias Janvin

The presence of competing events, such as death, makes it challenging to define causal effects on recurrent outcomes. In this thesis, I formalize causal inference for recurrent events, with and without competing events. I define several causal estimands an ...

EPFL2023

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Matias Janvin, Mats Julius Stensrud

We summarize what we consider to be the two main limitations of the "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event" (Schmidli et al. 2022). First, the authors did not give detailed guidance on how to choose an appropriate esti ...

TAYLOR & FRANCIS INC2023

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Association for the Advancement of Artificial Intelligence (AAAI)2023

DISCO: Distilling Counterfactuals with Large Language Models

Antoine Bosselut, Zeming Chen, Qiyue Gao

Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsour ...

Assoc Computational Linguistics-Acl2023

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Léo Nicolas René Meynent, Maxime Jean Julien Peyrard, Robert West, Valentin Hartmann

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

IEEE COMPUTER SOC2023

Causal modelling of heavy-tailed variables and confounders with application to river flow

Valérie Chavez, Anthony Christopher Davison

Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows a ...

SPRINGER2022

Minimum Cost Intervention Design for Causal Effect Identification

Seyed Jalal Etesami, Negar Kiyavash, Sina Akbari

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...

PMLR2022

Revisiting the General Identifiability Problem

Seyed Jalal Etesami, Negar Kiyavash, Ehsan Mokhtarian, Yaroslav Kivva

We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show t ...

2022

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

Scale effects on efficiency and profitability in the Swiss banking sector

Andreas Fuster

This paper analyzes efficiency and profitability in the Swiss banking sector over the period 1997–2019. We find strong evidence for scale economies: for most banks in the sample, efficiency and profitability increase with bank size. Using an instrumental v ...

2022

Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

Yuqing Liu, Xianlin Wang

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (AT ...

2022

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