Category
Causality
Related publications (124)
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 ...
2023Causal Influences over Social Learning Networks
This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that rev ...
2023Interventionist 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 INC2023Novel 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)2023DISCO: 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-Acl2023Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Alexandre Massoud Alahi, Yuejiang Liu
Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited to simple synthetic settings that are far away from real-world problems. In t ...
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 SOC2023Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect
Negar Kiyavash, Ehsan Mokhtarian, Sina Akbari, Fateme Jamshidi, Seyed Jalal Etesami
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these algorithms hinges on the restrictive assumption of having access to a correc ...
2022Causal modelling of heavy-tailed variables and confounders with application to river flow
Anthony Christopher Davison, Valérie Chavez
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