
	Where results make sense

Topic: Causal models

Related Topics

Standard Model

OSI model

mathematical model

model airplanes

relational model

business model

model railway

Unified Modeling Language

model theory

fashion model

Beretta 92 Models

In the News (Thu 16 Feb 12)

EXECUTIVE POWER

Iran

ANALYSIS

Pakistan

Family compact

	PhilSci Archive - Stable Models and Causal Explanation in Evolutionary Biology
		Glymour, Bruce (2006) Stable Models and Causal Explanation in Evolutionary Biology.
		Abstract: Models that fail to satisfy the Markov condition are unstable in the sense that changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error.
		Since this is true both for group and individual level models, models of neither sort correctly represent the causal structure generating, nor correctly explain, the phenomena of interest.
	philsci-archive.pitt.edu /archive/00003021 (170 words)

	Causal Models and Emergent Population Effects (Site not responding. Last check: 2007-10-17)
		Population transmission models are causal models with a markedly different focus and structure from the sufficient-component cause model.
		The importance of causal models that capture phenomenon in the network plane that the sufficient-component cause model misses is further emphasized by the example where a gene affected infection risk.
		Dynamic models of population processes are needed that incorporate exposure patterns, time, and contact networks in the manner of transmission models while also incorporating the joint effects of multiple exposures in individuals in the manner of the sufficient-component cause model.
	www.sph.umich.edu /~jkoopman/CausModCom/APHACom.htm (4379 words)

	Causal Disanalogy I: Strong Models & Theoretical Expectations
		While strong models are generally recognized to be ideal models, some researchers have asserted the actual existence of causal isomorphisms, and this is part of a tradition extending back to the writings of Claude Bernard.
		For such programs, the existence of causal disanalogies will be of variable importance: likely they will have less of an impact on the basic research, but will be more directly relevant to evaluation of the applied aspects of that research.
		As a result of evolution, causal properties (and structures and mechanisms) found in the systems of members of one species may be absent in members of another species; for example, rats lack gall bladders.
	www.stpt.usf.edu /hhl/papers/Strong.Models.htm (4063 words)

	Structural Equation Modeling
		Although it is not absolutely necessary, it is highly desirable that you have some background in factor analysis before attempting to use structural modeling.
		One cannot prove that a model is true — to assert this is the fallacy of affirming the consequent.
		What causal modeling does allow us to do is examine the extent to which data fail to agree with one reasonably viable consequence of a model of causality.
	www.statsoft.com /textbook/stsepath.html (1374 words)

	Causal Effect Models for Intention to Treat and Realistic Individualized Treatment Rules
		An important class of models in causal inference are the so-called marginal structural models which model the comparison between counterfactual outcome distributions corresponding with a static treatment intervention, conditional on user supplied baseline covariates, based on observing a longitudinal data structure on a sample of n independent and identically distributed experimental units.
		The definition of an intention to treat causal effect requires a user-supplied definition of a time-dependent process keeping track of the possible treatment options for an experimental unit, and, if that is not available, it may be derived from a fitted treatment mechanism.
		In addition causal effect models for realistic individualized treatment rules are presented which always map in the set of possible treatment options and are thereby also fully identifiable from the data; in particular it is shown that these models can be chosen to generalize marginal structural models.
	works.bepress.com /mark_van_der_laan/31 (459 words)

	Environmental Health \| Full text \| Causal models in epidemiology: past inheritance and genetic future
		These models are worth mentioning, because they have added some layers of complexity to the discussion on causality and have also contributed to solving some outstanding issues, including a more sophisticated approach to confounding and identification of intermediate variables.
		According to Pearl [7], a causal graph "is a directed acyclic graph (DAG) in which the vertices (nodes) of the graph represent variables and the directed edges (arrows) represent direct causal effects" (Figure 1 is an example).
		In Model B, G exacerbates the outcome of E. For example, xeroderma pigmentosum is an autosomal recessive disorder in which exposure to ultraviolet (UV) light causes a high incidence of skin cancers, due to a defect of DNA repair.
	www.ehjournal.net /content/5/1/21 (6631 words)

	Causal Mapping (Site not responding. Last check: 2007-10-17)
		Causal mapping refers to the use of directed node and link graphs -- similar to concept maps in some ways -- to represent a set of causal relationships within a system.
		Causal mapping uses a representation that is similar that those used by dynamic modeling environments like STELLA and Model-It.
		Their goal was to build a model of water quality that they could use to explain what would affect the health of a local stream.
	cilt.berkeley.edu /synergy/causalmap (468 words)

	Revising Models of Gene Regulation
		To this end, we have focused on qualitative causal models, which describe relations between quantitative variables (such as gene expression levels) but only specify the direction of the causal influence and its sign, not the functional form or parameters.
		Qualitative causal models provide a useful middle ground between quantitative models, which require many observations to fit parameters, and Bayesian networks, which, as typically used, rely on methods for discretizing continuous variables.
		Our approach to modeling gene regulation has much in common with that taken by Clark Glymour and his colleagues, but differs in its focus on qualitative causal models, revision of those models, and interactive modeling environments.
	www.isle.org /~langley/regulate.html (1130 words)

	CHI 97: Mind Maps and Causal Models: Using Graphical Representations of Field Research Data
		A "causal loop diagram" was also developed to document the team's understanding of the internal and external driving forces for each organization.
		In traditional causal modeling, a network of variables is developed and the causal relationships between variables are explicitly delineated.
		Although generating each of the causal models was extremely difficult, we quickly saw the benefits of the process.
	sigchi.org /chi97/proceedings/poster/mil.htm (1154 words)

	Models of Computation
		For an causal model of computation, it remains to explain how it is determined which operations to apply to which operands and in which order.
		For an causal model of computation, we cannot take the processors as fl-boxes, but have to look into them and say how they determine which consume events and produce events to cause and in which order.
		For an causal model of computation, it remains to explain how the agent determins which events to broadcast and how to update its state, and in which order.
	web.cs.mun.ca /~ulf/mod/moc.html (2590 words)

	Analysis Ch5 pg2 Causal Confounding Models
		At least two, for roofing and income, are by themselves significantly associated with wt/age (models 2 and 6), thus are acting as useful controls.
		In model 4 DLOWEDN is now entered, and indeed the coefficient size and significance of both water and sanitation falls substantially.
		The interpretation of model 4 is that we can no longer be sure whether or not piped water has an effect on wt/age when education is taken into account, because education is correlated both with water supply and with wt/age.
	www.tulane.edu /~panda2/Analysis2/Multi-way/multi02.htm (1786 words)

	White, B
		Successful models have a low level of complexity (number of objects and processes), employ causal reasoning (a simple mechanism for how something operates), and have an intermediate level of abstraction (semantic distance between the model and the thing the model represents).
		Intermediate models are a link between different levels of abstraction (iconic and symbolic) and different model perspectives (charged particle — microscopic vs. electrical circuits — macroscopic).
		Intermediate models introduce students to scientific domains by a progression of increasingly complex causal models starting at an intermediate level of abstraction.
	kie.berkeley.edu /transitions/constructivism.html (819 words)

	Theoretical Foundations
		Statistical causal inference is the task of inferring features of the causal processes that generated data from statistical properties of the sample and background knowledge.
		Because of linearity, the causal structure of an RSEM induces constraints on the population that a Bayesian network with the same causal structure might not.
		Mixtures of populations in which causal connections are in opposite directions: During a given period of time, some people may be exposed to a chemical because of their employment status, and some people may change their employment status because of their exposure to the chemical.
	www.phil.cmu.edu /projects/tetrad/tet3/chp2.htm (5789 words)

	Causal Models
		Causal Models and Diagrammatic Representations of the Operation of Causal Processes
		When a diagram shows a connection between "compatibility" and the "affective quality of interaction," the diagram proposes that couples who are highly compatible (compared to those who are less compatible) are more likely to express affection and less likely to experience conflict and negativity.
		It is sometimes useful to think through the implications of your causal model by applying it to a particular hypothetical case.
	www.utexas.edu /research/pair/causal1.htm (516 words)

	Two Models of Models
		On this view the primary function of animal tests is to uncover the causal mechanisms which produce and direct the course of a disease or condition in animals.
		Thus, there is probabilistic causality within the (non-human) laboratory population, probabilistic causality within the human population outside the laboratory, and an uncertainty about whether the results observed in the non-human animal population will be (statistically) relevant to the human biomedical phenomena of interest.
		True, a physiologist might describe the operations of the liver in causal terms (e.g., the mechanisms whereby it removes a foreign substance from the blood) or in functional terms (as purifying the blood), depending on the purpose in hand.
	www.stpt.usf.edu /hhl/papers/2models.htm (7133 words)

	Model Selection in Causal Models (Site not responding. Last check: 2007-10-17)
		In recursive models response variables of some explanatory variables cannot be explanatory variables to the same explanatory variables, i.e., there cannot in the graph be any oriented cycles, cycles where one goes along undirected edges and in the direction of arrows.
		Model selection methods for creating a diagnostic system by causal models on discrete variables are discussed in Lauritzen:Thiesson:Spiegelhalter:92.
		In the following the fact will be used stating that in block-recursive models the test of the two variables conditionally independent can be performed given explanatory variables to the two variables and ignoring response variables .
	www.math.aau.dk /~jhb/Thesis/PartIII/node233.html (306 words)

	UAI 2006 - Invited Tutorials
		In modern applications, however, the value of model compilation has been quite influential in exploiting the local (parameteric) structure of models, which usually incurrs too much overhead to justify its exploitation by standard inference approaches (unless the local structure is extreme).
		Theoretical results on model compilation have also presented a unifying framework for classical inference algorithms, based on jointrees, variable elimination and conditioning.
		This tutorial will cover the subject of model compilation from both theortical and practical perspectives, providing an exposition to state of the art algorithms, and discussing the impact it has had on scaling up exact inference to levels never attained before.
	www.ics.uci.edu /~csp/uai2006/tutorials.html (441 words)

	EKSL Research: Causal Modeling (Site not responding. Last check: 2007-10-17)
		In addition, we want to build a causal model which reflects the relationships existing in a dataset, therefore methods of evaluating how well a model fits the data are imperative.
		The resulting causal model is a directed acyclic graph with the dependent variable as the root.
		It is a commonly held belief that multiple regression techniques are ill-suited for causal induction due to the effects of latent variables and common causes, but preliminary results from FBD suggest otherwise.
	eksl-www.cs.umass.edu /research/causal-modeling.html (600 words)

	Causal Models
		A model in which the value of a variable at aparticular time can be determined without reference to that variable is said to be causal.
		As an example of a model that is not causal, consider a company that attempts to set price in order to achieve a revenue goal.
		Such a system is not causal, and although a mathematical solution to the problem might exist, Vensim will report this as an error and make no attempt to solve it (see Chapter 3).
	www.vensim.com /documentation/html/22000.htm (253 words)

	Selmon Tutorial (Site not responding. Last check: 2007-10-17)
		A causal model characterizes a physical system in terms of state variables and causal influence relations among the variables.
		In the causal graph defined by the state variables and influence relations, changes in any variable may be propagated to other variables through the influence relations.
		Causal simulation is the process of tracking changes in variables and propagating them to other variables through influence relations, thereby producing a new set of changes.
	www-aig.jpl.nasa.gov /public/mdt/technology/SELMON/selmon_tutorial.html (1813 words)

	Tetrad Project Homepage
		The TETRAD programs describe causal models in three distinct parts or stages: a picture, representing a directed graph specifying hypothetical causal relations among the variables; a specification of the family of probability distributions and kinds of parameters associated with the graphical model; and a specification of the numerical values of those parameters.
		Update models of categorical data; i.e.,, compute the probability of any variable in the model conditional on any set of values for other variables in the model.
		Predict the probability of a variable in a model (without latent variables) from interventions that fix or randomize values for any set of other variables in the model.
	www.phil.cmu.edu /projects/tetrad (434 words)

	Graphical Models
		If there is a causal path from A to B, then A is an ancestor of B, and B is a descendant of A. If a variable has no parents in the graph, it is exogenous, otherwise it is endogenous.
		Once you have your causal graph --- whether through estimation or through simply being handed one --- you can do lots of great things with it, like predict the effects of manipulating some of the variables, or make backward inferences from effects to causes.
		Of course it doesn't really solve the problem of establishing causal relations, in the way Hume objected to; it says, assuming there are causal relations, of a certain stochastic form, and that these are stable, then they can be learned.
	bactra.org /notebooks/graphical-models.html (1730 words)

	Causal Performance Models
		Although causality is still a highly debated issue among statisticians, we believe it provides a natural and intuitive representation about the relations among variables.
		We propose the use of causal models for modeling how variables influence the overall performance of (parallel) applications.
		One of the hardest criticisms of causality and the learning algorithms are the assumptions that are made.
	parallel.vub.ac.be /research/causalModels/index.html (437 words)

	The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology - The MIT Press (Site not responding. Last check: 2007-10-17)
		The representations used in the emerging theory are causal Bayes nets or graphical causal models.
		In his new book, Clark Glymour provides an informal introduction to the basic assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples.
		Using Bayes net techniques, Glymour suggests novel experiments to distinguish among theories of human causal learning and reanalyzes various experimental results that have been interpreted or misinterpreted--without the benefit of Bayes nets and graphical causal models.
	mitpress.mit.edu /0262072203 (241 words)

	Table of contents for Causal models (Site not responding. Last check: 2007-10-17)
		The causal modeling framework and levels of causality Part 2: Evidence and application VI.
		The psychology of judgment: Causality is pervasive A.
		Causal models as a psychological theory: Knowledge is qualitative B.
	www.loc.gov /catdir/toc/ecip056/2004031000.html (251 words)

	Explanatory Pluralism (Site not responding. Last check: 2007-10-17)
		Recall that while other theoretical models were supposed to pick out some relevant features of evolutionary change, MLS was supposed to be the model that correctly described the causal structure of natural selection.
		The models that represent selection as happening on a single level are just ``toys.'' Sure they make causal claims, but we should take these with a grain of salt.
		If the essential feature of MLS is, as I have argued, that it picks out the correct causal structure responsible for natural selection, then it is difficult or impossible to endorse explanatory pluralism about it and other causal models.
	www-csli.stanford.edu /~weisberg/Pluralism/node3.html (1901 words)

	Amazon.com: The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology: Books: Clark Glymour (Site not responding. Last check: 2007-10-17)
		In several senses, causal relations are, or ought to be, the subjects of cognitive psychology.
		In particular, causal Bayes nets can be used to successfully model the 'theory theory' which was developed to understand how cognition develops in infants and children.
		This model has dominated psychological theories of human and animal learning for many years, but the author discusses an example that indicates problems with this model.
	www.amazon.com /Minds-Arrows-Graphical-Causal-Psychology/dp/0262072203 (2093 words)

	Scientific Problems with Animal Models
		Animal models of human conditions tend to provide only the most obvious and general information, such as that cancers kill; in order for them to provide reliable and specific information, the model and the human condition must have identical causal factors and have no significant systemic differences that affect these causal factors.
		Convinced that his animal model precisely paralleled the human disease, he concluded that human polio was introduced to the brain via the nose and confined to the central nervous system.
		Animal models of polio were not very helpful as causal models, and they significantly delayed development of an effective vaccine.
	www.curedisease.com /Perspectives/vol_4_1993/sci-prob.htm (2946 words)

Try your search on: Qwika (all wikis)