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Theory unification and graphical models in human categorization
"... Disparate, mutually incompatible theories of categorization are widespread in cognitive psychology. While there are various formal results connecting pairs of these theories, the primary research focus has been on particular empirical tests of people’s favorite theories. This chapter steps back from ..."
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Disparate, mutually incompatible theories of categorization are widespread in cognitive psychology. While there are various formal results connecting pairs of these theories, the primary research focus has been on particular empirical tests of people’s favorite theories. This chapter steps back from the question of which single theory (if any) is “right, ” and focuses
Graphical models for inference under outcomedependent sampling
 STAT SCI 2010;25:368–87
, 2010
"... We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in casecontrol studies. Graphical models represent assumptions about the conditional independencies among the variables. By including a no ..."
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We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in casecontrol studies. Graphical models represent assumptions about the conditional independencies among the variables. By including a node for the sampling indicator, assumptions about sampling processes can be made explicit. We demonstrate how to read off such graphs whether consistent estimation of the association between exposure and outcome is possible. Moreover, we give sufficient graphical conditions for testing and estimating the causal effect of exposure on outcome. The practical use is illustrated with a number of examples.
Identification and likelihood inference for recursive linear models with correlated errors
, 2007
"... In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equati ..."
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In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equation systems, and in Gaussian graphical modelling. We show that recursive linear models that are ‘bowfree’ are wellbehaved statistical models, namely, they are everywhere identifiable and form curved exponential families. Here, ‘bowfree ’ refers to models satisfying the condition that if a variable x occurs in the structural equation for y, then the errors for x and y are uncorrelated. For the computation of maximum likelihood estimates in ‘bowfree ’ recursive linear models we introduce the Residual Iterative Conditional Fitting (RICF) algorithm. Compared to existing algorithms RICF is easily implemented requiring only least squares computations, has clear convergence properties, and finds parameter estimates in closed form whenever possible. 1
IS THERE A SIGNALLING ROLE FOR PUBLIC WAGES? EVIDENCE FOR THE EURO AREA BASED ON MACRO DATA 1
, 1148
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Dora, a Robot Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Object Search ∗
"... Dora, the robot, is trying to find object in its environment. Instead of just exhaustively searching everywhere, Dora is equipped with probabilistic reasoning, representations, and planning to exploit uncertain commonsense knowledge, such as that cornflakes are usually found in kitchens, while also ..."
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Dora, the robot, is trying to find object in its environment. Instead of just exhaustively searching everywhere, Dora is equipped with probabilistic reasoning, representations, and planning to exploit uncertain commonsense knowledge, such as that cornflakes are usually found in kitchens, while also accounting for the uncertainty of sensing in the realworld. Dora demonstrates how to combine task and observation planning in the presence of uncertainty by autonomously switching between contingent and sequential planning sessions. The demonstration emphasises the benefit of employing a robot with commonsense knowledge and the benefit of the switching planner.
2003]: ‘On World Poverty: Its Causes and Effects
 Food and Agricultural Organization (FAO) of the United Nations, Research Bulletin
, 2003
"... Recent advances in modeling directed acyclic graphs are used to sortout causal patterns among a set of thirteen measures deemed relevant to the incidence of world poverty. Crosssection measures of the percent of population living on one and two dollars or less per day from eighty low income countr ..."
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Recent advances in modeling directed acyclic graphs are used to sortout causal patterns among a set of thirteen measures deemed relevant to the incidence of world poverty. Crosssection measures of the percent of population living on one and two dollars or less per day from eighty low income countries are exposed to a battery of tests of conditional independence with respect to measures of economic and political freedom, income inequality, income per person, agricultural income, child mortality, birth rate, life expectancy, relative size of rural population, illiteracy rate, foreign aid as a percentage of national income, international trade as a percentage of national income and percentage of population that is undernourished. Motivation for the method of analysis precedes results. Results are presented as a graph that shows our measures of economic and political freedom, income inequality, illiteracy and agricultural income to be exogenous movers of poverty when measured as the percent of the population living on two dollars or less per day. Foreign aid and international trade are not connected to the other variables in the graph. Results on our measure of extreme poverty (people living on one dollar or less per day) show that such populations are immune from improvements in economic progress of the general economy. The “rising tide lifts all boats ” argument apparently doesn’t cover the extreme poor of our sample.
On Block Ordering of Variables in Graphical Modelling
, 2004
"... In graphical modelling, the existence of substantive background knowledge on block ordering of variables is used to perform structural learning within the family of chain graphs in which every block corresponds to an undirected graph and edges joining vertices in different blocks are directed in acc ..."
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In graphical modelling, the existence of substantive background knowledge on block ordering of variables is used to perform structural learning within the family of chain graphs in which every block corresponds to an undirected graph and edges joining vertices in different blocks are directed in accordance with the ordering. We show that this practice may lead to an inappropriate restriction of the search space and introduce the concept of labelled block ordering B corresponding to a family of Bconsistent chain graphs in which every block may be either an undirected graph or a directed acyclic graph or, more generally, a chain graph. In this way we provide a flexible tool for specifying subsets of chain graphs, and we observe that the most relevant subsets of chain graphs considered in the literature are families of Bconsistent chain graphs for the appropriate choice of B. Structural learning within a family of Bconsistent chain graphs requires to deal with Markov equivalence. We provide a graphical characterisation of equivalence classes of Bconsistent chain graphs, namely the Bessential graphs, as well as a procedure to construct the Bessential graph for any given equivalence class of Bconsistent chain graphs. Both largest chain graphs and essential graphs turn out to be special cases of Bessential graphs.
MINIMAL SUFFICIENT CAUSATION AND DIRECTED ACYCLIC GRAPHS
, 2009
"... Notions of minimal sufficient causation are incorporated within the directed acyclic graph causal framework. Doing so allows for the graphical representation of sufficient causes and minimal sufficient causes on causal directed acyclic graphs while maintaining all of the properties of causal directe ..."
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Notions of minimal sufficient causation are incorporated within the directed acyclic graph causal framework. Doing so allows for the graphical representation of sufficient causes and minimal sufficient causes on causal directed acyclic graphs while maintaining all of the properties of causal directed acyclic graphs. This in turn provides a clear theoretical link between two major conceptualizations of causality: one counterfactualbased and the other based on a more mechanistic understanding of causation. The theory developed can be used to draw conclusions about the sign of the conditional covariances among variables.
A new approach to argument by analogy: extrapolation and chain graphs. Paper presented at the Philosophy of Science Biannual Meeting
"... In order to make scientific results relevant to practical decision making, it is often necessary to transfer a result obtained in one set of circumstances—an animal model, a computer simulation, an economic experiment—to another that may differ in relevant respects—for example, to humans, the global ..."
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In order to make scientific results relevant to practical decision making, it is often necessary to transfer a result obtained in one set of circumstances—an animal model, a computer simulation, an economic experiment—to another that may differ in relevant respects—for example, to humans, the global climate, or an auction. Such inferences,
of LaborCausal Analysis after Haavelmo
"... Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The ..."
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Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. IZA Discussion Paper No. 7628