Metadata-Version: 1.1
Name: causalinfo
Version: 1.2.0
Summary: Information Measures on Causal Graphs.
Home-page: https://github/brettc/causalinfo/
Author: Brett Calcott
Author-email: brett.calcott@gmail.com
License: MIT
Description: ============================================
        ``causalinfo``: Information on Causal Graphs 
        ============================================
        
        .. image:: https://badge.fury.io/py/causalinfo.png
            :target: http://badge.fury.io/py/causalinfo
        
        `causalinfo` is a Python library to aid in experimenting with different
        *information measures on causal graphs*---a combination of information
        theory with recent work on causal graphs [Pearl2000]_. These information
        measures can used to ascertain the degree to which one variable controls or
        explains other variables in the graph. The use of these measures has important
        connections to work on causal explanation in philosophy of science, and to
        understanding information processing in biological networks. 
        
        The library is a work in progress, and will be extended as research continues.
        
        What does it do?
        ----------------
        
        `causalinfo` has been written primarily for interactive use within `IPython
        Notebook`_. You can create variables and assign probability distributions to
        them, or relate them to other variables using conditional probabilities.
        Several related variables can be combined into a directed acyclic graph, which
        can generate a joint distribution for all variables under observation, or
        under controlled interventions on certain variables. You can also calculate
        various information measures between variables in the graph whilst controlling
        other variables. These include correlative measures, such as Mutual
        Information, but also causal measures, such as Information Flow
        [AyPolani2008]_, and Causal Specificity [GriffithsEtAl2015]_.
        
        For some brief examples of how to use the library, please see the IPython Notebooks
        that are included:
        
        * Introduction_. A short introduction to some of the things you can do with
          the library.
        
        * Rain_. Performing some interventions on a causal graph; an example from
          Judea Pearl's book.
        
        * Signaling_. Measuring Causation in Signaling Networks. Some examples from
          [CalcottEtAl2016]_.
        
        * `Information Flow`_. Measuring the flow of information in Causal networks
          from [AyPolani2008]_.
        
        .. _Introduction: https://github.com/brettc/causalinfo/blob/master/notebooks/introduction.ipynb
        
        .. _Rain: https://github.com/brettc/causalinfo/blob/master/notebooks/rain.ipynb
        
        .. _Signaling: https://github.com/brettc/causalinfo/blob/master/notebooks/signaling.ipynb
        
        .. _`Information Flow`: https://github.com/brettc/causalinfo/blob/master/notebooks/ay_polani.ipynb
        
        
        .. TODO: Add a getting started guide
        .. Getting Started
            ---------------
            .. code:: bash 
            pip install causalinfo
            curl https://raw.githubusercontent.com/brettc/causalinfo/master/notebooks/introduction.ipynb 
        
        Some Caveats
        ------------
        
        The library is not meant for large scale analysis. The code has been written
        to offload as much as possible on to other libraries (such as Pandas_ and
        Networkx_), and to allow easy inspection of what is going on within `IPython
        Notebook`_, thus it is not optimized for speed. Calculating the joint
        distribution for a causal graph with many variables can become very *slow*
        (especially if the variables have many states). 
        
        
        Authorship
        ----------
        
        All code was written by `Brett Calcott`_.
        
        
        Acknowledgments
        ---------------
        
        This work is part of the research project on the `Causal Foundations of
        Biological Information`_ at the `University of Sydney`_, Australia. The work
        was made possible through the support of a grant from the Templeton World
        Charity Foundation. The opinions expressed are those of the author and do not
        necessarily reflect the views of the Templeton World Charity Foundation. 
        
        License
        -------
        
        MIT licensed. See the bundled LICENSE_ file for more details.
        
        
        .. Miscellaneous Links------------
        
        .. _LICENSE: https://github.com/brettc/causalinfo/blob/master/LICENSE
        
        .. _`Brett Calcott`: http://brettcalcott.com
        
        .. _`University of Sydney`: http://sydney.edu.au/ 
        
        .. _`IPython Notebook`: http://ipython.org/notebook.html 
        
        .. _Pandas: http://pandas.pydata.org/
        
        .. _Networkx: https://networkx.github.io/ 
        
        .. _`Causal Foundations of Biological Information`: http://sydney.edu.au/foundations_of_science/research/causal_foundations_biological_information.shtml 
        
        
        References
        ----------
        
        .. [AyPolani2008] Ay, N., & Polani, D. (2008). Information flows in causal
            networks. Advances in Complex Systems, 11(01), 17–41.
        
        .. [GriffithsEtAl2015] Griffiths, P. E., Pocheville, A., Calcott, B., Stotz, K., 
            Kim, H., & Knight, R. (2015). Measuring Causal Specificity. Philosophy of Science, 82(October), 529–555.
        
        .. [CalcottEtAl2016] Calcott, B., Griffiths, P. E., Pocheville, A.
            (Forthcoming). Signals that Make a Difference. British Journal for Philosophy of Science.
        
        .. [Pearl2000] Pearl, J. (2000). Causality. Cambridge University Press. 
        
        
        .. vim: fo=tcroqn tw=78
        
Keywords: causal,information,network
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Mathematics
