34,597 research outputs found
Online Causal Structure Learning in the Presence of Latent Variables
We present two online causal structure learning algorithms which can track
changes in a causal structure and process data in a dynamic real-time manner.
Standard causal structure learning algorithms assume that causal structure does
not change during the data collection process, but in real-world scenarios, it
does often change. Therefore, it is inappropriate to handle such changes with
existing batch-learning approaches, and instead, a structure should be learned
in an online manner. The online causal structure learning algorithms we present
here can revise correlation values without reprocessing the entire dataset and
use an existing model to avoid relearning the causal links in the prior model,
which still fit data. Proposed algorithms are tested on synthetic and
real-world datasets, the latter being a seasonally adjusted commodity price
index dataset for the U.S. The online causal structure learning algorithms
outperformed standard FCI by a large margin in learning the changed causal
structure correctly and efficiently when latent variables were present.Comment: 16 pages, 9 figures, 2 table
Adaptation of WASH Services Delivery to Climate Change and Other Sources of Risk and Uncertainty
This report urges WASH sector practitioners to take more seriously the threat of climate change and the consequences it could have on their work. By considering climate change within a risk and uncertainty framework, the field can use the multitude of approaches laid out here to adequately protect itself against a range of direct and indirect impacts. Eleven methods and tools for this specific type of risk management are described, including practical advice on how to implement them successfully
Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods
Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a
suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address
these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of
NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital
asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of
NTFPs is represented in the model as the conversion of one form of capital asset into another, which is
influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are
determined by the availability of the five types of assets following commercialization. The model,
implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP
case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes
of success and failure in NTFP commercialization, and can be used to explore the potential impacts of
policy options and other interventions on livelihoods. The potential value of this approach for the
development of NTFP theory is discussed
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