Concept

Modèle causal de Neyman-Rubin

Publications associées (53)

CauseOccam: Learning Interpretable Abstract Representations in Reinforcement Learning Environments via Model Sparsity

"I choose this restaurant because they have vegan sandwiches" could be a typical explanation we would expect from a human. However, current Reinforcement Learning (RL) techniques are not able to provide such explanations, when trained on raw pixels. RL alg ...

2021

Estimating an extreme Bayesian network via scalings

Mario Krali

A recursive max-linear vector models causal dependence between its components by expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph and some exogenous innovation. Motivated by extreme value theory, inno ...

ELSEVIER INC2021

A platform for experimental precision medicine: The extended BXD mouse family

Johan Auwerx, Evan Graehl Williams

The challenge of precision medicine is to model complex interactions among DNA variants, phenotypes, development, environments, and treatments. We address this challenge by expanding the BXD family of mice to 140 fully isogenic strains, creating a uniquely ...

CELL PRESS2021

Causal inference in continuous time: an example on prostate cancer therapy

Mats Julius Stensrud, Pal Christie Ryalen

In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time mar ...

2020

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

A causal framework for classical statistical estimands in failure-time settings with competing events

Mats Julius Stensrud

In failure-time settings, a competing event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate canc ...

2020

A Graphical Description of Partial Exchangeability

Mats Julius Stensrud, Aaron Leor Sarvet

Partial exchangeability is sufficient for the identification of some causal effects of interest. Here we review the use of common graphical tools and the sufficient component cause model in the context of partial exchangeability. We illustrate the utility ...

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

Conditional separable effects

Mats Julius Stensrud, Aaron Leor Sarvet

Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality o ...

2020

Causal inference with limited resources: proportionally-representative interventions

Mats Julius Stensrud, Aaron Leor Sarvet

Investigators often evaluate treatment effects by considering settings in which all individuals are assigned a treatment of interest, assuming that an unlimited number of treatment units are available. However, many real-life treatments are of limited supp ...

2020

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