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    <title>Pinboard (mraginsky)</title>
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    <description>recent bookmarks from mraginsky</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s10701-019-00307-6"/>
	<rdf:li rdf:resource="https://royalsocietypublishing.org/doi/10.1098/rsif.2012.0869"/>
	<rdf:li rdf:resource="https://ideas.repec.org/a/hom/homoec/v20y2003p195-255.html"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/BF00258078"/>
	<rdf:li rdf:resource="http://www.jmlr.org/papers/v10/white09a.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1203.6502"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0911.0280"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1110.5429"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1108.3984"/>
	<rdf:li rdf:resource="http://www.cs.cmu.edu/~bziebart/#publications"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1008.4805"/>
	<rdf:li rdf:resource="http://dresdencodak.com/2009/02/16/exorcising-laplaces-demon/"/>
	<rdf:li rdf:resource="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/05/causal_inferenc_4.html"/>
	<rdf:li rdf:resource="http://diva.library.cmu.edu/webapp/simon/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1002.1446"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0910.5561"/>
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  </channel><item rdf:about="https://link.springer.com/article/10.1007/s10701-019-00307-6">
    <title>How Downwards Causation Occurs in Digital Computers | SpringerLink</title>
    <dc:date>2023-07-28T16:51:19+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10701-019-00307-6</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Digital computers carry out algorithms coded in high level programs. These abstract entities determine what happens at the physical level: they control whether electrons flow through specific transistors at specific times or not, entailing downward causation in both the logical and implementation hierarchies. This paper explores how this is possible in the light of the alleged causal completeness of physics at the bottom level, and highlights the mechanism that enables strong emergence (the manifest causal effectiveness of application programs) to occur. Although synchronic emergence of higher levels from lower levels is manifestly true, diachronic emergence is generically not the case; indeed we give specific examples where it cannot occur because of the causal effectiveness of higher level variables.]]></description>
<dc:subject>papers to-read causality physics systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:cbf8c3aebecc/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:physics"/>
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<item rdf:about="https://royalsocietypublishing.org/doi/10.1098/rsif.2012.0869">
    <title>The algorithmic origins of life | Journal of The Royal Society Interface</title>
    <dc:date>2022-08-02T16:00:01+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/10.1098/rsif.2012.0869</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Although it has been notoriously difficult to pin down precisely what is it that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and, in particular, that top-down (or downward) causation—where higher levels influence and constrain the dynamics of lower levels in organizational hierarchies—may be a major contributor to the hierarchal structure of living systems. Here, we propose that the emergence of life may correspond to a physical transition associated with a shift in the causal structure, where information gains direct and context-dependent causal efficacy over the matter in which it is instantiated. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.]]></description>
<dc:subject>papers to-read biogenesis information-theory algorithms causality control-theory dynamical-systems cybernetics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:053e8aa6fdff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:biogenesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:cybernetics"/>
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<item rdf:about="https://ideas.repec.org/a/hom/homoec/v20y2003p195-255.html">
    <title>Dependency Equilibria and the Casual Structure of Decision and Game Situations</title>
    <dc:date>2022-06-15T07:53:27+00:00</dc:date>
    <link>https://ideas.repec.org/a/hom/homoec/v20y2003p195-255.html</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The paper attempts to rationalize cooperation in the one-shot prisoners' dilemma (PD). It starts by introducing (and preliminarily investigating)á a new kind of equilibrium (differing from Aumann's correlated equilibria) according to which the players' actions may be correlated (sect. 2). In PD the Pareto-optimal among these equilibria is joint cooperation. Since these equilibria seem to contradict causal preconceptions, the paper continues with a standard analysis of the causal structure of decision situations (sect. 3).The analysis then raises to a reflexive point of view according to which the agent integrates his own present and future decision situations into the causal picture of his situation (sect. 4). This reflexive structure is first applied to the toxin puzzle and then to Newcomb's problem, showing a way to rationalize drinking the toxin and taking only one box without assuming causal mystery /sect. 5). The latter result is finally extended to a rationalization of cooperation in PD (sect. 6).]]></description>
<dc:subject>papers to-read causality probability game-theory decision-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:86f4df8059d3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:decision-theory"/>
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<item rdf:about="https://link.springer.com/article/10.1007/BF00258078">
    <title>Stochastic independence, causal independence, and shieldability | SpringerLink</title>
    <dc:date>2022-06-15T07:22:30+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/BF00258078</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The aim of the paper is to explicate the concept of causal independence between sets of factors and Reichenbach's screening-off-relation in probabilistic terms along the lines of Suppes' probabilistic theory of causality (1970). The probabilistic concept central to this task is that of conditional stochastic independence. The adequacy of the explication is supported by proving some theorems about the explicata which correspond to our intuitions about the explicanda.]]></description>
<dc:subject>papers to-read philosophy-of-science causality probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d4e7b2de567c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:probability"/>
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<item rdf:about="http://www.jmlr.org/papers/v10/white09a.html">
    <title>Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning</title>
    <dc:date>2017-03-22T15:56:32+00:00</dc:date>
    <link>http://www.jmlr.org/papers/v10/white09a.html</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Judea Pearl's Causal Model is a rich framework that provides deep insight into the nature of causal relations. As yet, however, the Pearl Causal Model (PCM) has had a lesser impact on economics or econometrics than on other disciplines. This may be due in part to the fact that the PCM is not as well suited to analyzing structures that exhibit features of central interest to economists and econometricians: optimization, equilibrium, and learning. We offer the settable systems framework as an extension of the PCM that permits causal discourse in systems embodying optimization, equilibrium, and learning. Because these are common features of physical, natural, or social systems, our framework may prove generally useful for machine learning. Important features distinguishing the settable system framework from the PCM are its countable dimensionality and the use of partitioning and partition-specific response functions to accommodate the behavior of optimizing and interacting agents and to eliminate the requirement of a unique fixed point for the system. Refinements of the PCM include the settable systems treatment of attributes, the causal role of exogenous variables, and the dual role of variables as causes and responses. A series of closely related machine learning examples and examples from game theory and machine learning with feedback demonstrates some limitations of the PCM and motivates the distinguishing features of settable systems.]]></description>
<dc:subject>papers to-read causality control-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:113d7c9fda33/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:control-theory"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1203.6502">
    <title>[1203.6502] Quantifying causal influences</title>
    <dc:date>2012-03-30T02:14:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.6502</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["Common methods of causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution of all these variables, the DAG contains all information about how intervening on one variable would change the distribution of the other n-1 variables. It remains, however, a non-trivial question how to quantify the causal influence of one variable on another one. 
Here we propose a measure for causal strength that refers to direct effects and measure the "strength of an arrow" or a set of arrows. It is based on a hypothetical intervention that modifies the joint distribution by cutting the corresponding edge. The causal strength is then the relative entropy distance between the old and the new distribution. 
We discuss other measures of causal strength like the average causal effect, transfer entropy and information flow and describe their limitations. We argue that our measure is also more appropriate for time series than the known ones. 
Finally, we discuss conceptual problems in defining the strength of indirect effects."

There is no mention of directed information, and yet their measure of causal strength seems to be closely related to it.]]></description>
<dc:subject>to-read causality information-theory feedback-information-theory papers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:0a27a06351d8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:feedback-information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
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</item>
<item rdf:about="http://arxiv.org/abs/0911.0280">
    <title>[0911.0280] Causal Inference on Discrete Data using Additive Noise Models</title>
    <dc:date>2012-03-01T01:18:57+00:00</dc:date>
    <link>http://arxiv.org/abs/0911.0280</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive noise models have been suggested to approach the case of continuous variables. In many situations, however, the variables of interest are discrete or even have only finitely many states. In this work we extend the notion of additive noise models to these cases. We prove that whenever the joint distribution $prob^{(X,Y)}$ admits such a model in one direction, e.g. $Y=f(X)+N, N independent X$, it does not admit the reversed model $X=g(Y)+tilde N, tilde N independent Y$ as long as the model is chosen in a generic way. Based on these deliberations we propose an efficient new algorithm that is able to distinguish between cause and effect for a finite sample of discrete variables. In an extensive experimental study we show that this algorithm works both on synthetic and real data sets.]]></description>
<dc:subject>papers to-read machine-learning causality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:mraginsky/b:f66368951c66/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1110.5429">
    <title>[1110.5429] Causal modeling and inference for electricity markets</title>
    <dc:date>2011-10-26T00:40:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.5429</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oil, gas and coal prices, with German wind power and Nordic water reservoir levels as exogenous variables, we estimate a causal model for the price dynamics, both for contemporaneous and lagged relationships. In contemporaneous time, Nordic and German electricity prices are interlinked through gas prices. In the long run, electricity prices and British gas prices adjust themselves to establish the equlibrium price level, since oil, coal, continental gas and EUR/USD are found to be weakly exogenous.
]]></description>
<dc:subject>to-read causality economics markets papers</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:930fc79e1cca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:markets"/>
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<item rdf:about="http://arxiv.org/abs/1108.3984">
    <title>[1108.3984] Process Dimension of Classical and Non-Commutative Processes</title>
    <dc:date>2011-08-22T15:23:05+00:00</dc:date>
    <link>http://arxiv.org/abs/1108.3984</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read probability markov-chains dynamical-systems causality via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:16467af7925d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:markov-chains"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:via:cshalizi"/>
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<item rdf:about="http://www.cs.cmu.edu/~bziebart/#publications">
    <title>Brian Ziebart -- Purposeful Adaptive Behavior Prediction</title>
    <dc:date>2011-04-25T18:44:44+00:00</dc:date>
    <link>http://www.cs.cmu.edu/~bziebart/#publications</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>people homepages research papers ai machine-learning game-theory prediction reinforcement-learning information-theory causality</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:88124fb50d4b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:machine-learning"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reinforcement-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
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<item rdf:about="http://arxiv.org/abs/1008.4805">
    <title>[1008.4805] Space-time and special relativity from causal networks</title>
    <dc:date>2011-01-04T03:28:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1008.4805</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA["We show how the Minkowskian space-time emerges from a topologically homogeneous causal network, presenting a simple analytical derivation of the Lorentz transformations, with metric as pure event-counting. The derivation holds generally for d=1 space dimension, however, it can be extended to d>1 for special causal networks."
]]></description>
<dc:subject>papers to-read special-relativity physics causality</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:d4f07e84993b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:special-relativity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
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</item>
<item rdf:about="http://dresdencodak.com/2009/02/16/exorcising-laplaces-demon/">
    <title>Dresden Codak » Archive » Exorcising Laplace’s Demon</title>
    <dc:date>2010-11-19T14:11:12+00:00</dc:date>
    <link>http://dresdencodak.com/2009/02/16/exorcising-laplaces-demon/</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>comics fun-stuff geekery philosophy-of-science causality</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:329df49e5ad1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:comics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:fun-stuff"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:geekery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/05/causal_inferenc_4.html">
    <title>Causal inference in economics - Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2010-05-21T14:55:13+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2010/05/causal_inferenc_4.html</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>economics causality statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:ab98213fc1cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://diva.library.cmu.edu/webapp/simon/">
    <title>DIVA2: Search: Herbert Simon</title>
    <dc:date>2010-04-29T23:32:05+00:00</dc:date>
    <link>http://diva.library.cmu.edu/webapp/simon/</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[The Herbert Simon collection at the CMU library
]]></description>
<dc:subject>papers reference economics organizations causality AI complexity complex-systems computer-science</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:8ebf911c5e61/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:papers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:reference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:mraginsky/t:organizations"/>
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<item rdf:about="http://arxiv.org/abs/1002.1446">
    <title>[1002.1446] On directed information theory and Granger causality graphs</title>
    <dc:date>2010-02-09T01:16:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1002.1446</link>
    <dc:creator>mraginsky</dc:creator><dc:subject>papers to-read causality information-theory feedback-information-theory graphical-models</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:9841bcf7c75a/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/0910.5561">
    <title>[0910.5561] Distinguishing Cause and Effect via Second Order Exponential Models (Dominik Janzing, Xiaohai Sun, Bernhard Schoelkopf)</title>
    <dc:date>2009-11-02T23:19:19+00:00</dc:date>
    <link>http://arxiv.org/abs/0910.5561</link>
    <dc:creator>mraginsky</dc:creator><description><![CDATA[We propose a method to infer causal structures containing both discrete and continuous variables. The idea is to select causal hypotheses for which the conditional density of every variable, given its causes, becomes smooth. We define a family of smooth densities and conditional densities by second order exponential models, i.e., by maximizing conditional entropy subject to first and second statistical moments. If some of the variables take only values in proper subsets of R^n, these conditionals can induce different families of joint distributions even for Markov-equivalent graphs.
]]></description>
<dc:subject>papers to-read statistics causality graphical-models</dc:subject>
<dc:identifier>https://pinboard.in/u:mraginsky/b:af882dda4c73/</dc:identifier>
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