456 research outputs found
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 of background theory and a survey of methods for
structure discovery. We primarily focus on modern, continuous optimization
methods, and provide reference to further resources such as benchmark datasets
and software packages. Finally, we discuss the assumptive leap required to take
us from structure to causality.Comment: 35 page
GPU-accelerated stochastic predictive control of drinking water networks
Despite the proven advantages of scenario-based stochastic model predictive
control for the operational control of water networks, its applicability is
limited by its considerable computational footprint. In this paper we fully
exploit the structure of these problems and solve them using a proximal
gradient algorithm parallelizing the involved operations. The proposed
methodology is applied and validated on a case study: the water network of the
city of Barcelona.Comment: 11 pages in double column, 7 figure
Evaluating temporal observation-based causal discovery techniques applied to road driver behaviour
Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the
application of causal discovery techniques. However, as it stands observational causal discovery
techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity
typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing
observational techniques and promote further discussion of these topics we carry out a benchmark
across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets
in addition to those generated synthetically we highlight where improvements need to be made in
order to facilitate the application of causal discovery techniques to the aforementioned use-cases.
Finally, we discuss potential directions for future work that could help better tackle the difficulties
currently experienced by state of the art techniques
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