13,234 research outputs found
Quantifying information transfer and mediation along causal pathways in complex systems
Measures of information transfer have become a popular approach to analyze
interactions in complex systems such as the Earth or the human brain from
measured time series. Recent work has focused on causal definitions of
information transfer excluding effects of common drivers and indirect
influences. While the former clearly constitutes a spurious causality, the aim
of the present article is to develop measures quantifying different notions of
the strength of information transfer along indirect causal paths, based on
first reconstructing the multivariate causal network (\emph{Tigramite}
approach). Another class of novel measures quantifies to what extent different
intermediate processes on causal paths contribute to an interaction mechanism
to determine pathways of causal information transfer. A rigorous mathematical
framework allows for a clear information-theoretic interpretation that can also
be related to the underlying dynamics as proven for certain classes of
processes. Generally, however, estimates of information transfer remain hard to
interpret for nonlinearly intertwined complex systems. But, if experiments or
mathematical models are not available, measuring pathways of information
transfer within the causal dependency structure allows at least for an
abstraction of the dynamics. The measures are illustrated on a climatological
example to disentangle pathways of atmospheric flow over Europe.Comment: 20 pages, 6 figure
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Conditional independence testing is a fundamental problem underlying causal
discovery and a particularly challenging task in the presence of nonlinear and
high-dimensional dependencies. Here a fully non-parametric test for continuous
data based on conditional mutual information combined with a local permutation
scheme is presented. Through a nearest neighbor approach, the test efficiently
adapts also to non-smooth distributions due to strongly nonlinear dependencies.
Numerical experiments demonstrate that the test reliably simulates the null
distribution even for small sample sizes and with high-dimensional conditioning
sets. The test is better calibrated than kernel-based tests utilizing an
analytical approximation of the null distribution, especially for non-smooth
densities, and reaches the same or higher power levels. Combining the local
permutation scheme with the kernel tests leads to better calibration, but
suffers in power. For smaller sample sizes and lower dimensions, the test is
faster than random fourier feature-based kernel tests if the permutation scheme
is (embarrassingly) parallelized, but the runtime increases more sharply with
sample size and dimensionality. Thus, more theoretical research to analytically
approximate the null distribution and speed up the estimation for larger sample
sizes is desirable.Comment: 17 pages, 12 figures, 1 tabl
Quantifying information transfer and mediation along causal pathways in complex systems
Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer aimed at decompositions of predictive information about a target variable, while excluding effects of common drivers and indirect influences. While common drivers clearly constitute a spurious causality, the aim of the present article is to develop measures quantifying different notions of the strength of information transfer along indirect causal paths, based on first reconstructing the multivariate causal network. Another class of novel measures quantifies to what extent different intermediate processes on causal paths contribute to an interaction mechanism to determine pathways of causal information transfer. The proposed framework complements predictive decomposition schemes by focusing more on the interaction mechanism between multiple processes. A rigorous mathematical framework allows for a clear information-theoretic interpretation that can also be related to the underlying dynamics as proven for certain classes of processes. Generally, however, estimates of information transfer remain hard to interpret for nonlinearly intertwined complex systems. But if experiments or mathematical models are not available, then measuring pathways of information transfer within the causal dependency structure allows at least for an abstraction of the dynamics. The measures are illustrated on a climatological example to disentangle pathways of atmospheric flow over Europe
Better Milk than Cola: Soft Drink Taxes and Substitution Effects
Soft Drink Taxes, Obesity, Substitution effects, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety, Health Economics and Policy, H, H2, H25,
HOW TO PAY FOR AGRICULTURAL INCOME SUPPORTS
This paper examines the impact of deficit reductions on agricultural price support spending, and proposes several policy innovations designed to remove agricultural programs from the entitlement category. First, agricultural program costs are analyzed in relation to deficit reduction requirements resulting from recent legislation. Second, price support and food stamp programs are proposed as a separate budget category to be financed through an excise tax on retail food sales. Third, the incidence of this tax on consumers is considered, and compensation for lower income groups is proposed via expanded eligibility for the food stamp program. A final section presents some conclusions for policy.Agricultural and Food Policy, Agricultural Finance,
ECONOMIC IMPLICATIONS OF WIDER COMPENSATION FOR "TAKINGS." OR, WHAT IF AGRICULTURAL POLICIES RULED THE WORLD?
Agricultural and Food Policy,
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