3 research outputs found
Rejoinder
Rejoinder of "Statistical Inference: The Big Picture" by R. E. Kass
[arXiv:1106.2895]Comment: Published in at http://dx.doi.org/10.1214/11-STS337REJ the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Direct and Indirect Effects -- An Information Theoretic Perspective
Information theoretic (IT) approaches to quantifying causal influences have
experienced some popularity in the literature, in both theoretical and applied
(e.g. neuroscience and climate science) domains. While these causal measures
are desirable in that they are model agnostic and can capture non-linear
interactions, they are fundamentally different from common statistical notions
of causal influence in that they (1) compare distributions over the effect
rather than values of the effect and (2) are defined with respect to random
variables representing a cause rather than specific values of a cause. We here
present IT measures of direct, indirect, and total causal effects. The proposed
measures are unlike existing IT techniques in that they enable measuring causal
effects that are defined with respect to specific values of a cause while still
offering the flexibility and general applicability of IT techniques. We provide
an identifiability result and demonstrate application of the proposed measures
in estimating the causal effect of the El Ni\~no-Southern Oscillation on
temperature anomalies in the North American Pacific Northwest