198 research outputs found
Identifiability of causal graphs under nonadditive conditionally parametric causal models
Causal discovery from observational data is a very challenging, often
impossible, task. However, estimating the causal structure is possible under
certain assumptions on the data-generating process. Many commonly used methods
rely on the additivity of the noise in the structural equation models.
Additivity implies that the variance or the tail of the effect, given the
causes, is invariant; the cause only affects the mean. In many applications, it
is desirable to model the tail or other characteristics of the random variable
since they can provide different information about the causal structure.
However, models for causal inference in such cases have received only very
little attention.
It has been shown that the causal graph is identifiable under different
models, such as linear non-Gaussian, post-nonlinear, or quadratic variance
functional models. We introduce a new class of models called the Conditional
Parametric Causal Models (CPCM), where the cause affects the effect in some of
the characteristics of interest.We use the concept of sufficient statistics to
show the identifiability of the CPCM models, focusing mostly on the exponential
family of conditional distributions.We also propose an algorithm for estimating
the causal structure from a random sample under CPCM. Its empirical properties
are studied for various data sets, including an application on the expenditure
behavior of residents of the Philippines
09501 Abstracts Collection -- Software Synthesis
From 06.12.09 to 11.12.09, the Dagstuhl Seminar 09501 ``Software Synthesis \u27\u27 in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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