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D'ya like dags? a survey on structure learning and causal discovery
Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …
causal relationships from data, we need structure discovery methods. We provide a review …
Inferring causation from time series in Earth system sciences
J Runge, S Bathiany, E Bollt, G Camps-Valls… - Nature …, 2019 - nature.com
The heart of the scientific enterprise is a rational effort to understand the causes behind the
phenomena we observe. In large-scale complex dynamical systems such as the Earth …
phenomena we observe. In large-scale complex dynamical systems such as the Earth …
[HTML][HTML] Causal network reconstruction from time series: From theoretical assumptions to practical estimation
J Runge - Chaos: An Interdisciplinary Journal of Nonlinear …, 2018 - pubs.aip.org
Causal network reconstruction from time series is an emerging topic in many fields of
science. Beyond inferring directionality between two time series, the goal of causal network …
science. Beyond inferring directionality between two time series, the goal of causal network …
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
J Runge - … Conference on Artificial Intelligence and Statistics, 2018 - proceedings.mlr.press
Conditional independence testing is a fundamental problem underlying causal discovery
and a particularly challenging task in the presence of nonlinear dependencies. Here a fully …
and a particularly challenging task in the presence of nonlinear dependencies. Here a fully …
A simple measure of conditional dependence
M Azadkia, S Chatterjee - The Annals of Statistics, 2021 - JSTOR
We propose a coefficient of conditional dependence between two random variables Y and Z
given a set of other variables X 1,..., Xp, based on an iid sample. The coefficient has a long …
given a set of other variables X 1,..., Xp, based on an iid sample. The coefficient has a long …
The conditional permutation test for independence while controlling for confounders
TB Berrett, Y Wang, RF Barber… - Journal of the Royal …, 2020 - academic.oup.com
We propose a general new method, the conditional permutation test, for testing the
conditional independence of variables X and Y given a potentially high dimensional random …
conditional independence of variables X and Y given a potentially high dimensional random …
A survey on causal discovery methods for iid and time series data
The ability to understand causality from data is one of the major milestones of human-level
intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships …
intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships …
Auditing for human expertise
R Alur, L Laine, D Li, M Raghavan… - Advances in Neural …, 2023 - proceedings.neurips.cc
High-stakes prediction tasks (eg, patient diagnosis) are often handled by trained human
experts. A common source of concern about automation in these settings is that experts may …
experts. A common source of concern about automation in these settings is that experts may …
CCMI: Classifier based conditional mutual information estimation
S Mukherjee, H Asnani… - Uncertainty in artificial …, 2020 - proceedings.mlr.press
Abstract Conditional Mutual Information (CMI) is a measure of conditional dependence
between random variables X and Y, given another random variable Z. It can be used to …
between random variables X and Y, given another random variable Z. It can be used to …
Local permutation tests for conditional independence
I Kim, M Neykov, S Balakrishnan… - The Annals of …, 2022 - projecteuclid.org
Local permutation tests for conditional independence Page 1 The Annals of Statistics 2022, Vol.
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …
50, No. 6, 3388–3414 https://doi.org/10.1214/22-AOS2233 © Institute of Mathematical Statistics …