Review of causal discovery methods based on graphical models
A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …
causal relations and make use of them. Causal relations can be seen if interventions are …
Causal discovery and inference: concepts and recent methodological advances
This paper aims to give a broad coverage of central concepts and principles involved in
automated causal inference and emerging approaches to causal discovery from iid data and …
automated causal inference and emerging approaches to causal discovery from iid data and …
Learning sparse nonparametric dags
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs)
from data. Our approach is based on a recent algebraic characterization of DAGs that led to …
from data. Our approach is based on a recent algebraic characterization of DAGs that led to …
Gradient-based neural dag learning
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from
observational data. We adapt a recently proposed continuous constrained optimization …
observational data. We adapt a recently proposed continuous constrained optimization …
Deep end-to-end causal inference
Causal inference is essential for data-driven decision making across domains such as
business engagement, medical treatment and policy making. However, research on causal …
business engagement, medical treatment and policy making. However, research on causal …
Causal discovery from heterogeneous/nonstationary data
It is commonplace to encounter heterogeneous or nonstationary data, of which the
underlying generating process changes across domains or over time. Such a distribution …
underlying generating process changes across domains or over time. Such a distribution …
On the identifiability and estimation of causal location-scale noise models
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the
effect $ Y $ can be written as a function of the cause $ X $ and a noise source $ N …
effect $ Y $ can be written as a function of the cause $ X $ and a noise source $ N …
Causal autoregressive flows
Two apparently unrelated fields—normalizing flows and causality—have recently received
considerable attention in the machine learning community. In this work, we highlight an …
considerable attention in the machine learning community. In this work, we highlight an …
Causal structure-based root cause analysis of outliers
Current techniques for explaining outliers cannot tell what caused the outliers. We present a
formal method to identify" root causes" of outliers, amongst variables. The method requires a …
formal method to identify" root causes" of outliers, amongst variables. The method requires a …
Learning functional causal models with generative neural networks
We introduce a new approach to functional causal modeling from observational data, called
Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural …
Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural …