Concept
Causality
Related publications (121)
Causal inference with recurrent and competing events
Mats Julius Stensrud, Pal Christie Ryalen, Matias Janvin
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 ...
SPRINGER2023Interventionist estimands in event history analysis
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 ...
EPFL2023We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"
Mats Julius Stensrud, Matias Janvin
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 INC2023DISCO: 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-Acl2023Novel 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)2023Distribution Inference Risks: Identifying and Mitigating Sources of Leakage
Robert West, Maxime Jean Julien Peyrard, Valentin Hartmann, Léo Nicolas René Meynent
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 SOC2023Cycling Interaction Rituals in the Conflict against the Car. From the Bike Subculture to the City Scale and Beyond
Mobility scholars have long been interested in challenging the automobile's hegemony in the street, particularly by highlighting how to develop urban cycling. The article contributes to this task by explaining how one type of conflict between the bike and ...
Abingdon2023Minimum Cost Intervention Design for Causal Effect Identification
Negar Kiyavash, Sina Akbari, Seyed Jalal Etesami
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