C a u s a l i t y. A theory of energy, time and space.
ISBN: 9781409229520 Volume 1. Fifth Edition 2008. 552 pages. Paperback. $69.50
ISBN: 9781409229520 Volume 2. Fifth Edition 2008. 320 pages. Paperback. $49.50
 
Publishing company: Lulu : http://www.lulu.de/ 

Last Update: 
June 09^{th}, 2007. 

Please, choose among ... 

Marketing : Web Stores, Fax ... 

2006 
Biometrics 
XXIII International Biometric Conference, Montreal, Canada. 

http://www.ibc2006.org/e/01home_e.shtml 

This conference brings together statisticians and others interested in the development and application of statistical and mathematical methods for the biological sciences. 

My contribution: 

New Method for Calculating Causal Relationships. 

I. Barukcic, Jever, Germany 

The Scientific Program (pdf) is now available ! >>>> page 41. 

http://www.ibc2006.org/pdf/sciprog1May2006.pdf. 

http://www.ibc2006.org/pdf/sciprog29June2006.pdf, p. 49, p. 53. 

http://www.ibc2006.org/pdf/sciprog29June2006.pdf. 

2007 
Quantum Mechanics 
Quantum Theory: Reconsideration of Foundations  4 

June 1116, 2007 

Växjö University, 

Sweden. 

My contribution: 

Bell's Theorem  A fallacy of the excluded middle. 

http://www.vxu.se/msi/icmm/qtrf4/program.pdf p. 6 

Second Edition 2006. 
Ilija Barukcic. Causality. New Statistical Methods. Second Edition, 2006. (*.pdf file) 
September, 7^{th} 2006: The second Edition is available! 
The second Editions of my book is available. If you should have any problems to get my book, please use the fax order (download it from this web page) or please feel free to contact me or the publishing company. It is possible to help you. Last Update: 07.09.2006. 
First Edition 2005. 
I am sorry, the first Edition 2005 is no longer available. 
Erratum first Edition: Ilija Barukcic Causality. New Statistical Methods. 2005. (*.pdf file). 
Pictures: Ilija Barukcic. Die Kausalität. 1997. (*.pdf file) Ilija Barukcic. (*.pdf file) Get Adobe Reader at http://www.adobe.de/products/acrobat/readstep2_allversions.html to study the *.pdf files above. Last revision: February 22^{th} 2006. 
2006 

Montreal Canada July 1621 2006 

Presentation of the new methods at the conference in Montreal. 

2006 

Book Review Marry E Thompson 

ISI  INTERNATIONAL STATISTICAL INSTITUTE 

http://isi.cbs.nl/ 

ISI  Short Book Reviews 

... 

(Book) Reviews 

2006 
Comments on Thompson's Book Review, April 2006 
Get a book for free for a Book Review (at http://www.bod.de/presse/rezensionsexemplare.html ): Condition: A Book Review must be published! Help me on request for Book Review (*.GIF File) 
( With The Reader ) 

26.05.2006 
Jack Himelright: 

29.10.2006: 
Lofti A Zadeh 

Mail: Order Barukcic's Causality. Please contact me if you need some calculations on your data. 
Webstores 
The central idea behind my publication and the aim to characterise the relationship between cause and effect using the tools of classical logic, probability theory, philosophy, statistics is based on the fact, that causes have influence on the probabilities of their effects (Suppes, Hesslow, ...). But my publication is not simply a new probabilistic theory of causation, it is much more then this. The basic work on the unified field theory is done too. Einstein's and Heisenberg's equations can be derived directly from my equations. Einstein's field equations have to do a lot with causation. Causality is absolutely compatible with Einstein's Relativity and with Heisenberg's Uncertainty Principle. This is proofed by me very precisely and is published in the second Edition of my monograph above. Causality does not only provide new methods for extracting causal relationships from experimental and nonexperimental data. Causality is an outstanding contribution to the Philosophy of Science, to Classical/Mathematical Logic and Scientific Methods, to Mathematical Statistics and Probability and to Physics and to ... 
Finally, a unified mathematical and statistical model of causation is mathematically proofed and available. 
In this sense, the asymmetry of causation is often understood that way, that causes must precede their effects in time. Only, can an effect as such occur in the absence of a cause? Can a random variable be a cause of an effect in the absence of an effect? To what extent may a cause as such be independent from an effect? Until my publication, it was generally unclear and mathematically not proofed, can or to what extent can causes precede their effects in time? 
Probabilistic theories of causation do indeed require that a cause has influence on the probability of its effect. Are such theories of causation compatible with indeterminism at all or is causation at the end incompatible with probability and at the end with indeterminism? Is it necessary to develop a theory of causation that does not presuppose determinism? My book gives substantial mathematical and philosophical answers to the mathematical and philosophical problem of causality. 
The mathematical and statistical problem of causality is definitely s o l v e d ! 
Causality is mathematised, the probabilistic approach to causation has won the race. 
Based on the deterministic relationship between cause and effect, Boole's, mathematical and classical logic, at the top of the formal development of the ( statistical and probabilistic ) theory of causation, the mathematical formula of the causal relationship c is derived and mathematically proofed! 
Making causal inferences on the basis of experimental and nonexperimental data is possible, it doesn't matter if the random variables have a discrete, a continuous or a mixed distribution. It doesn't matter if the mathematical formula of the causal relationship c is used in Epidemiology, in Sociology, in Biostatistics, in Economy, in Medicine, in Nuclear physics, in ... etc. 
Methods for the calculation of the critical value of the causal relationship c are developed. Methods for the calculation of the p value, of beta and power of the causal relationship c (left, right, two sided) are developed. Thus, a so called test statistic is available to proof data for causality. 
In a reanalysis based on the published data of Naoumi Uemura et al, N Engl J Med 345 (2001) 11, 784, i have proofed, that the bacterium Helicobacter pylori is the cause of human gastric cancer ( p value = 0.001999831220, power > 0.85 ). This was published by me August 2005 and thus long before the Nobel Prize announcement by Professor Hans Jörnvall, Secretary of the Nobel Committee for Physiology or Medicine, on October 3, 2005, that the Nobel Prize in Physiology or Medicine 2005 will be awarded to Barry J. Marshall and J. Robin Warren "for their discovery of the bacterium Helicobacter pylori and its role in gastritis and peptic ulcer disease" ( > http://nobelprize.org/medicine/laureates/2005/index.html ). 
In so far, it is me who has found/identified the cause of human gastric cancer, based on the data of Naoumi Uemura et al, and no other then me! 
Comments On The Study Of Wong et al. 
In a reanalysis based on the data of Mathilde Bonnet et al. ( Journal of the National Cancer Institute, Vol. 91, No 16 (1999) 13761381), i have proofed, that EpsteinBarr virus (EBV) is a cause and not the cause of human invasive breast carcinoma ( p value = 0.000321, power > 0.99 ). According to this investigation, not all, but just about 50 % of all human invasive breast carcinoma are definitely caused by EpsteinBarr virus. 
The one and only assumption based on the determination of cause and effect is that the random variables must be Bernoulli distributed (or Bernoulli distributed in character). Thus, any density function, any (empirical) cumulative distribution function, any probability mass function etc. of random variables can be used for calculating causal relationships without any restriction. Needless to say, the quality of data must be assured. 
My new methods must be distinguished from other publications especially from the procedures of Judea Pearl, Peter Spirtes, Clark Glymour and Richard Scheines. To many explicit assumptions that are rarely satisfied in realistic cases must be made before the procedures of Judea Pearl, Peter Spirtes, Clark Glymour and Richard Scheines can yield results, if at all. In so far, at the end, this procedures are somehow not that much useful for solving causality problems. 
Causation vs. Correlation 
In contrast to causation, the central idea behind correlation is the equivalence of a Quantity X and a Quantity Y or in other words Quantity X = Quantity Y Or Y = X. Let E(X) denote the expectation values of X. Let E(Y) denote the expectation values of Y. Thus, according to Kolmogoroff, it is true that E( Y ) = E ( X ). After some calculations, we do obtain the formula of the correlation coefficient of a population as (Barukcic, Causality. ..., p. 340343) r = s ( Y, X ) / ( s ( Y ) * s ( X ) ). The correlation coefficient states only, that there is a linear relationship between a Quantity X and another Quantity Y and not that a Quantity X is the cause of a Quantity Y. It is known, that correlation analysis is based on the cum hoc, ergo propter hoc fallacy. Under certain circumstances, a Quantity X can be a cause of a Quantity Y, only this can not be proofed with the correlation coefficient. In this case, it is necessary to use my methods to succeed. Correlation has indeed nothing to do with causation or with the new mathematical formula of the causal relationship c. 
Conditions vs. Counterfactuals 
Conditio per quam vs. risk ratio 
In Epidemiology, Biostatistics etc. the risk ratio is a very important relationship. Thus let us regard the following contingency table. 
The risk ratio (RR) is defined as 
RR = ( a / ( a + b )) / ( c / ( c + d ) ). 
Recall: Exposure proportion in cases = a / ( a + c ). Exposure proportion in controls = b / ( b + d ). Odds ratio : OR = ( ( a * d ) / ( b * c ) ). 
Let us perform a thought experiment (from the German term Gedankenexperiment, coined by Ernst Mach) 
Please allow me to presuppose, that the most of the reader will be able to remember that it is raining sometimes. On the other hand, the most of us will have observed, that the rain changes somehow other random variables too. Thus, let us ask our self: is there a relationship between raining and a street that is wet? Or in other words, 
is the rain a risk factor of a wet street? 
In this case the following should hold true: 
Let us perform n=100 Bernoulli trials somewhere out there under haven where it is raining very often (f. e. the equator) and let us obtain f. e. the following result. 
The risk ratio RR in this case is RR = ( a / ( a + b )) / ( c / ( c + d ) ) or RR = ( 50/50)/ (49/50) = 1,02040816326530612244897959183673. The risk ratio RR = 1,02040816326530612244897959183673 or in other words, there is no effect of the rain on the street, there is no significant association between raining and a street that is wet. The street was exposed to the rain, but besides of this the rain as such had not any effect on the street, according to the investigation above and the risk ratio relationship. Do we really have another choice if we rely on the data above and on the risk ration then to conclude that 
the rain is not a risk factor of a wet street! 
But please stop for a while. Common sense must tell us, that this result is of course far away from reality. 
It is generally known, that when the rain falls, then the street is wet. The rain in this sense must be a risk factor of a wet street, absolutely. Thus, it is easily seen, that the capabilities of the risk ratio are very limited. We need a more reliable relationships, some that does not depend that much upon study design. Thus, let us calculate the probability of the conditio per quam relationship 
of the contingency table above. We obtain 
p ( It is Raining > The street is wet ) = 1, 
an absolutely significant result. Thus, we can say, it is true, that when it is raining, then the street is wet. 
The same data allow us two conclusions that are contrary! 
Let us perform another n=100 Bernoulli trials somewhere out there ( e. g. desert like Sahara without that much rain) under haven and let us obtain f. e. the following result. 
The risk ratio RR in this case is RR = ( a / ( a + b )) / ( c / ( c + d ) ) or RR = ( 1/1)/ (98/99) = 1,01020408163265306122448979591837. The risk ratio RR = 1,01020408163265306122448979591837 and once again, there is no effect of the rain on the street, there is no significant association A second investigation proofed the relationship between raining and a wet street and the result is the same there is no significant association. In so far, it seems to me that it is time to accept, that 
the rain is not a risk factor of a wet street! 
What is wrong? This two simple examples show us the dramatic errors that can happen, when we use some old relationships, especially the risk ratio. The risk ratio as such and some other similar old relationships too depend to much upon study design or on how cases and controls are sampled from the source population. They can lead to totally wrong results! Let us calculate the probability of the conditio per quam relationship of the contingency table above, thus we get p ( It is Raining > The street is wet ) = 1, an absolutely significant result. The conditio per quam is known since thousands of years and is not depending that much upon study design. The conditio per quam relationship is much more reliable then the risk ratio! This relationship is developed by me, mathematically proofed and can be used! 
Conditio sine qua non: Without A no B 
Sir William Richard Shaboe Doll, ( Doll R, Hill AB. Smoking and carcinoma of the lung. Preliminary report, British Medical Journal, 2: (1950) 739748 ), a British epidemiologist, was the first in the world who investigated in a historically important casecontrol study ( retrospektiv, Fall Kontroll Studie) the relationship between smoking and lung cancer. He found that 647 of 649 lung cancer cases were smokers. In contrast that, 622 of 649 noncancer controls were smokers. Let us have a look on these data in a 2by2 table. 
Is there a deterministic relationship between smoking and lung cancer?
The odds ratio OR can be calculated as OR = ( ( a * d ) / ( b * c ) ) OR = ( (647 * 27 ) / (622 * 2 ) ) OR = ( (17469 ) / (1244 ) ) OR = 14,04.. This result indicates a strong association. 
Let us reanalyse the data above and calculate the conditio sine qua non relationship ( Without <condition> no <conditioned>).
Hypothesis: Ho: p( Smoking < Lung Cancer ) = 1.0 HA: p( Smoking < Lung Cancer ) < 1.0 Alpha: 0.05, one sided test. Material and Methods 
Results p( Smoking < Lung Cancer ) = ( ... ) / ... p( Smoking < Lung Cancer ) = 0,99845916795069337442218798151002 p( Smoking < Lung Cancer ) critical = ... p( Smoking < Lung Cancer ) critical = ... p( Smoking < Lung Cancer ) critical = 0,988586208889688259139412043748017 Discussion The data above do support the Null hypothesis, that WITHOUT SMOKING NO LUNG CANCER because p( Smoking < Lung Cancer ) > p( Smoking < Lung Cancer ) critical.
Let us use the data above just for demonstration purposes to calculate the causal relationship. Hypothesis: Ho: c ( Smoking => Lung Cancer ) < 0 HA: c ( Smoking => Lung Cancer ) > 0 Alpha: 0.05, one sided test. Results c ( Smoking => Lung Cancer ) = + 0,130319738149084270086198603727516. > Z Value = 4,69512917056785193772410008292137 > p Value < 0,000005 c ( Smoking => Lung Cancer ) critical = ... / ... c ( Smoking => Lung Cancer ) critical = ... / ... c ( Smoking => Lung Cancer ) critical = 0,0456551644412469634423518250079336 Z Beta = ...  ... Z Beta = 3,05027554056785193772410008292137 p Beta ~ 0.001 Power = 1  p Beta = 1  0.001 = 0,999
Since c ( Smoking => Lung Cancer ) > c ( Smoking => Lung Cancer ) critical we must reject the Null hypothesis and accept the alternative hypothesis. There is a highly significant causal relationship between smoking and lung cancer ( p Value < 0,000005, power > 0.99 ). Discussion Thus, using the new mathematically proofed methods we can state. Firstly. Smoking is a conditio sine qua non of lung cancer or that is to say without smoking no lung cancer.
Please, pay attention. Smoking is not a conditio per quam of a lung cancer or in other words it is not that way, that when you smoke, then you must become lung cancer. Without smoking, a patient can't develop lung cancer. Secondly. Smoking is not only a condition of lung cancer, it is much more then this. Smoking is at the same time the cause and not one cause of lung cancer too ( p Value < 0,000005, power > 0.99 ), a highly significant result. Recall, the statistical power of a test is the probability of correctly rejecting a false H_{0} under certain distributional assumptions. 
Counterfactuals 
Counterfactual Theories of Causation 
are based more or less on the following assumption. Let c (=smoking) be the cause of the effect e (= lung cancer). According to the counterfactual theories of causation (David Hume, Lewis), if the cause c had not occurred, then the effect e would not have occurred too. In so far, this is not absolutely wrong. Still, there are many doubts on the adequacy of the analysis of causation in terms of counterfactuals. According to the counterfactual theories of causation, one cause of an effect is any event for which an effect would not have occurred. Example. Let a person develop lung cancer because of smoking. In terms of counterfactuals it is indeed true that if this person had not smoked this person would not have got lung cancer. In so far, not smoking is indeed thus an event for which an effect would not have occurred (not lung cancer). Only, what would happen, if the person above had not been born at all. It appears reasonable to accept, that if the person above had not been born or if the person above had not possessed lungs, the person above would not have got lung cancer too. Consequently, according to the counterfactual theories of causation, if the person above had not been born is equally an event for which an effect (lung cancer) would not have occurred and stands thus according to the counterfactual theories of causation in causal relation to lung cancer. Only, how can you get lung cancer, if you not born? On the other hand, there are a lot of persons who are born and they do not develop lung cancer. Being born as such is not a cause of lung cancer. We assumed above that smoking is the cause of lung cancer. According to the counterfactual theories of causation it is not smoking that is the cause of lung cancer, it is the fact of being born. According to the counterfactual theories of causation smoking is the cause of lung cancer and at the same time smoking is not a cause of lung cancer (being born is the cause) and both is true. We arrived at an impossible result. This is a logical contradiction. This simple reductio ad absurdum argues any relevance of the counterfactual approach to causation. 
Counterfactual Theories of Causation are generating to many absurd results and useless for solving the problem of causation. 
Boolean Algebra vs. Fuzzy Logic 
The fuzzy logic as such is not compatible with the fundamental laws of probability theory. This is proofed by me very precisely. Methods to use the rules of Boolean algebra for the values between 0 and 1 according to the fundamental laws of probability are developed and mathematically proofed. A Method to use the QuineMcCluskey Method on discrete and continuous random variables is developed and mathematically proofed. Thus, redundant variables can easily be identified, with the possibility to reduced complicated multirelationships to a few and simple one! 
Time series 
Many new Time series relationships are developed f. e. After a comes b ( After the day comes the night ...), a must before b, a before b. ... 
Structural equation modelling (SEM) 
Structural equation modelling (SEM) has dominated causal analysis for a long time. But even SEM researchers (Muthen 1987) have difficulties of finding the causal content of SEM. In so far, structural equation modelling (SEM) is widely (mis)used in biology, sociology, econometrics and other sciences. At the end, this rapidly growing and extremely complicated statistical machinery focuses today more or less only on model fitting. But even here, SEM fails. The widespread (mis) use of this complex and often poorly understood analytic methods is characterised by the trial to represent a causal theory by a path diagram and translation of this into a structural equation model like y = (ß*x) + e. There are so many concerns associated with structural equation modelling, where to start? Firstly. The world as such cannot be pressed into a linear or structural equation Model or being considered as only deterministic. Secondly. Structural equation modelling (SEM) is based on an absolutely insufficient approach to causation. Thirdly. Structural equation modelling (SEM) is incompatible with quantum mechanics and belongs today already to the past. Expensive training seminars are more or less necessary to learn this statistical techniques which at the end have nothing to do with causation. It appears to me that only a few individuals have benefited from using these statistical techniques (courses in structural equation modelling are sometimes very expensive ). Conclusion. Structural equation modelling (SEM) is a complicated statistical machinery and useless for discovering causeeffect relationships between variables of interest. The SEM starting point is not correct. > Cartwright N., (1995). Probabilities and Experiments, Journal of Econometrics 67: 4759. Structural equation modelling (SEM) in this sense is not worth loosing time, money and effort! ... 
Georg Wilhelm Friedrich Hegel 

International Virtual Library http://www.philo.de/VirtualLibrary/14.de.htm http://plato.stanford.edu/entries/hegel/ 

The International HegelSociety (Internationale HegelGesellschaft e.V.) 

Home: http://www.hegelgesellschaft.de/ 

The Hegel Society of Great Britain (HSGB) 

Home: http://www.hsgb.group.shef.ac.uk/index.html 

The Hegel Society of America 

http://www.hegel.org/index.html 

Science of Logic 

http://www.marxists.org/reference/archive/hegel/index.htm 

David Hume > http://plato.stanford.edu/entries/hume/ David Hume, An Enquiry Concerning Human Understanding (1748). Immanuel Kant: Akademieausgabe von Immanuel Kants Gesammelten Werken > http://www.ikp.unibonn.de/kant/verzeichnissegesamt.html 

Suppes, Patrick. > http://wwwphilosophy.stanford.edu/fss/ps.html 1970. A Probabilistic Theory of Causality. Amsterdam: NorthHolland. 

Germund Hesslow > http://www.mphy.lu.se/avd/nf/hesslow/philosophy/TWONOTES.htm Homepage > http://www.mphy.lu.se/avd/nf/hesslow/ Philosophy  selected publications > http://www.mphy.lu.se/avd/nf/hesslow/ 

Wolfgang Spohn > http://www.unikonstanz.de/FuF/Philo/Philosophie/Spohn/spohn.shtml Papers > http://www.unikonstanz.de/FuF/Philo/Philosophie/Spohn/spohn_papers.shtml David Kellogg Lewis (*September 28, 1941 – + October 14, 2001) > http://en.wikipedia.org/wiki/David_Lewis_(philosopher) Counterfactuals (1973 [revised printing 1986]; Blackwell & Harvard U.P.) Ellery Eells (1953  + August 10, 2006 ). > http://philosophy.wisc.edu/eells/ Ellery Eells, Probabilistic Causality. Cambridge University Press, 1991. Jon Williamson > http://www.kent.ac.uk/secl/philosophy/Staff/williamson.htm University of Kent, Canterbury. Nancy Cartwright > http://personal.lse.ac.uk/cartwrig/Default.htm Nancy Cartwright Recent Papers > http://personal.lse.ac.uk/cartwrig/Papers.htm University of California San Diego. Hitchcock, Christopher > http://www.hss.caltech.edu/people/faculty/cricky Hitchcock, Christopher. 1993. "A Generalized Probabilistic Theory of Causal Relevance." Synthese 97: 33564. John David Collins > http://collins.philo.columbia.edu/index.html Ned Hall > MIT L. A. Paul > http://www.u.arizona.edu/~lapaul/ John Collins, Ned Hall and L. A. Paul ISBN 0262532565. MIT PRESS. Douglas Kutach > http://www.brown.edu/Departments/Philosophy/Douglas_Kutach/index.html Robin Cowan > http://www.cgl.uwaterloo.ca/~racowan/cause.html Paul Noordhof > http://www.nottingham.ac.uk/philosophy/staff/paulnoordhof.htm Dialectical Materialism S Barker Counterfactuals, probabilistic counterfactuals and causation Mind 1999 108(431):427469 Charles A. Mercier: Causation 1916: > http://www.geocities.com/freasoner_2000/cause.htm Causation 1916 > http://evansexperientialism.freewebspace.com/mercier02.htm Logic MCMXII > http://www.geocities.com/freasoner_2000/logic.htm Logic MCMXII *.pdf: > http://www.cwru.edu/edocs/8/244.pdf 

The Causal Markov Condition 

N. Cartwright: "Against Modualrity, The Causal Markov Condition and Link Between the Two: Comments on Housman and Woodward" http://personal.lse.ac.uk/cartwrig/Papers.htm 

I. Martel: Indeterminism and the Causal Markov Condition http://www.erin.utoronto.ca/~imartel/Iain/Research.htm 

D. Steel: Indeterminism and the Causal Markov Condition http://www.msu.edu/user/steel/Ind&CMC.pdf 

Overview: http://classes.engr.oregonstate.edu/eecs/winter2006/cs539/slides/causalnetworks.pdf 

Berkovitz: On Causal Inference in Determinism and indeterminism http://www.umbc.edu/philosophy/berkovitz/papers/determinism.pdf 

The Principle of the Common Cause 

Berkovitz: On Causal Inference in Determinism and indeterminism 

http://www.umbc.edu/philosophy/berkovitz/papers/determinism.pdf 

I. Martel: Indeterminism and the Causal Markov Condition 

http://www.unikonstanz.de/ppm/workshop2/Martel_final.pdf 

Wolfgang Spohn: On Reichenbach's Principle of the Common Cause 

http://www.unikonstanz.de/FuF/Philo/Philosophie/Mitarbeiter/spohn_files/ wspohn22.pdf 

Philosophen und Philosophinnen 

http://philo.at/philpages/philosophen.html ( Prof. Herbert Hrachovec, Wien ) 

PhilSearch > http://search.freefind.com/ 

HegelLinks > http://hegel.net/en/links.htm 

Centre for Philosophy of Natural and Social Science : 

http://www.lse.ac.uk/collections/CPNSS/Default.htm 

London School of Economics 

http://www.lse.ac.uk/ 

3rd International Summer School 2004. University of Konstanz 

http://www.unikonstanz.de/ppm/summerschool2004/ 

James Robins 

http://www.hsph.harvard.edu/facres/rbns.html 

Research 

http://www.biostat.harvard.edu/~robins/research.html 

Judea Pearl 

http://bayes.cs.ucla.edu/jp_home.html 

Judea Pearl: Causality: models, reasoning and inference (Cambridge University Press, 2000). ISBN: 0521773628 

Peter Spirtes 

http://www.hss.cmu.edu/philosophy/facultyspirtes.php 

Clark Glymour 

http://www.informatics.indiana.edu/ra/presenters/glymour.asp 

Richard Scheines 

http://www.hss.cmu.edu/philosophy/facultyscheines.php 

Causation, Prediction, and Search, Mit Pr . ISBN: 0262194406 

The authors use the formalism of the Bayes networks to turn observations into causal knowledge but without the final success. 

Causality Lab: 

http://www.phil.cmu.edu/projects/causalitylab/ 

Richard E. Neapolitan 

http://www.neiu.edu/~reneapol/renpag1.htm 

Computer Science Dept., Northeastern Illinois University 

Larry Wasserman 

http://lib.stat.cmu.edu/~larry/ 

Carnegie Mellon University 

Mark van der Laan 

http://sph.berkeley.edu/faculty/vanderlaan.htm 

M. Elizabeth Halloran 

http://www.sph.emory.edu/~mehallo/ Emory University 

Thomas Richardson 

http://www.stat.washington.edu/tsr/ University of Washington 

Nanny Wermuth 

http://www.cs.chalmers.se/~wermuth/ Chalmers University of Gothenburg 

Rolf Haeni 

Bern University 

Phillip David 

University College London, DeGroot Prize, ... Bayesian Networks 

JEFFREY M. ALBERT 

http://epbiwww.case.edu/faculty/albert.html 

Dominik Janzing 

http://www.ira.uka.de/I3V_HTML/MITARBEITER/2335 

http://iakswww.ira.uka.de/ Karlsruhe 

Mathematics, Statistics, Probability: Org 

International Statistical Institute (ISI) 

Daniel Berze, Director 

http://isi.cbs.nl/perm.htm 

http://isi.cbs.nl/ 

International Biometric Society (IBS) 

http://www.tibs.org/ 

Bernoulli Society For Mathematical Statistics And Probability 

http://isi.cbs.nl/BS/bshome.htm 

American Mathematical Society 

http://www.ams.org/ 

European Mathematical Society 

http://www.emis.de/ 

International Epidemiology Association 

http://www.dundee.ac.uk/iea/ 

American College of Epidemiology 

http://www.acepidemiology2.org/societies/AAPES.asp 

International Society for Clinical Biostatistics 

http://www.iscb.org/ 

Institute of Mathematical Statistics 

http://www.imstat.org/ 

American Statistical Association 

http://www.amstat.org/ 

Deutsche Statistische Gesellschaft (DStatG) 

http://www.dstatg.de/ 

Deutsche Gesellschaft für Medizinische Informatik,Biometrie und Epidemiologie 

http://www.gmds.de/ 

Royal Statistical Society (RSS) 

http://www.rss.org.uk/ 

Biomathematics and Statistics Scotland 

http://www.bioss.sari.ac.uk/ 

Statistical Society of Canada 

http://www.ssc.ca/ 

Statistical Society of Australia 

http://www.statsoc.org.au/ 

The Probability Web 

http://www.mathcs.carleton.edu/probweb/probweb.html 

Statistical/Biostatistical Organisations 

http://biostatresources.com/id4.htm 

Second International Congress For The Unity Of Science, 1936 Copenhagen 

http://www.tuharburg.de/rzt/rzt/it/QM/schilpp.html 

Multidisciplinary Conference on Causality and Statistics, August 2000 

http://isi.cbs.nl/Bnews/00a/bn_5.html 

Joint International Conference on Cognitive Science 

Sydney, Australia. 13  17 July, 2003 

http://hps.arts.unsw.edu.au/cogsci_conf/index.html 

Workshop on Causality and Causal Discovery May 16^{th} 2004 

http://cs.uwindsor.ca/~ai04/causality.htm 

Vinterkonference 2006 

http://www.matstat.umu.se/aktuellt/vinterkonf/vkonf06/in_english.asp 

CAUSALITY AND PROBABILITY IN THE SCIENCES 

1416 June 2006, Keynes College Lecture Room 5, University of Kent, Canterbury, UK 

http://www.kent.ac.uk/secl/philosophy/jw/2006/capits.htm 

Empirical Evaluation of Causality, 26 June  30 June, 2006 

http://www.isr.umich.edu/cps/eitm/eitm2006/causality2006.html 

Conferences 

with my participation. 

XXIII International Biometric Conference 

(IBC 2006) July 16  21 2006, Montreal, Canada. 

http://www.ibc2006.org/pdf/sciprog29June2006.pdf 

http://www.ibc2006.org/pdf/sciprog29June2006.pdf, p. 49, p. 53, 

Quantum mechanics: Reconsideration of Foundations  4 

June 11  16, 2007 

Växjö University  Sweden. 

Relativistic Causality 

Conference in Honour of Abner Shimony 

http://perimeterinstitute.ca/activities/scientific/shimony/index.php 

http://en.wikipedia.org/wiki/Abner_Shimony 

Perimeter Institute, Waterloo, Ontario, Canada 

July 18  21, 2006 

CHSH inequality 

> http://en.wikipedia.org/wiki/CHSH_Bell_test 

Bell's inequality 

http://en.wikipedia.org/wiki/Bell_inequality 

Barukcic has refuted Bell's Theorem. 

Local hidden local variables exist. 

International Statistical Institute (ISI): 

http://isi.cbs.nl/AD.htm 

International Biometric Society 

http://www.tibs.org/advertising.htm 

Germany 

Internationale Biometrische Gesellschaft 

Deutsche Region 

http://www.biometrischegesellschaft.de/ 

Deutsche Gesellschaft für Medizinische Informatik,Biometrie und Epidemiologie 

http://www.gmds.de/stellenboerse/stellenboerse.php 

Weitere Stellenbörsen 

http://www.gmds.de/stellenboerse/weitere_links.php 

http://www.bfarm.de/de/DasBfArM/jobs/index.php 

http://www.dimdi.de/ 

http://www.gsf.de/epi/de/index_stellen.html 

I am managing an email list about causality. If you are not already on this list please drop some lines at Barukcic at tonline dot de You can be removed from this list the way you like it. There are no obligations or costs for you. I'm sorry, you can't know who is already on this email list. 

EMail: 
Barukcic < a_t > tonline < dot > de 
Handy: 
( 00 49 )  ( 0 ) 1 51  100 232 58 
Phone: 
( 00 49 )  44 23  99 11 11 
GMT: 
+ 1 h. 
Germany. 

AWARDS FOR RESEARCHERS 

Nobel prize 
http://nobelprize.org/ 
Balzan Prize 
http://www.balzan.com/ 
International Balzan Foundation 

CH8022 Zürich 

mailto: info@balzan.com 

Lakatos Award 
Philosophy of Science 
The London School of Economics and Political Science 

http://www.lse.ac.uk/ 

http://www.lse.ac.uk/collections/ 

The International Mathematical Union 

http://www.mathunion.org/ 

The Fields Medal 
http://www.fields.utoronto.ca/ 
Rolf Nevanlinna Prize 
http://www.mathunion.org/medals/Nevanlinna/index.html 
Carl Friedrich Gauss Prize for Applications of Mathematics 

http://www.mathunion.org/General/Prizes/Gauss/index.html 

The European Science Foundation 

The DeGroot Prize 
http://www.bayesian.org/awards/DeGrootPrize.html 
The Prize consists of an award of $1500 and a commemorative plaque. 

The Prize is awarded every two years. 

All books published no earlier than 5 years prior to the year of the DeGroot Prize competition may be considered. 

Authors may selfnominate, or may be nominated by another individual or individuals. 

MORTIMER SPIEGELMAN AWARD 
http://www.aphastat.dpcp.org/Spiegelman%20winners.htm 
Marvin Zelen Leadership Award in Statistical Science 
http://www.biostat.harvard.edu/events/awards/zelen/index.html#nominations 
Awards presented at the ASA Presidential Address Session 

Wilks Memorial Award 
http://www.amstat.org/awards/index.cfm?fuseaction=wilks 
The deadline for nominations is April 1 

Gottfried E. Noether Awards 
http://www.amstat.org/awards/index.cfm?fuseaction=noether (younger than 35 years of age) 
Outstanding Statistical Application 
http://www.amstat.org/awards/index.cfm?fuseaction=outstandingapplication 
The Award consists of a certificate and $1,000 cash prize. 

Nominations should be received by April 1, 2006. 

Ein Hinweis zu den gelinkten Seiten. 

Für den Inhalt der gelinkten Seiten Dritter übernehme ich grundsätzlich keine Haftung und auch keine Gewähr. Der Inhalt der gelinkten Seiten Dritter kann sich auch ändern ohne das ich das ständig überprüfe und an dieser Webpage zeitnah anpassen kann. Hinsichtlich des Inhalts der gelinkten Seiten Dritter ist selbstverständlich davon auszugehen, das ich mit dem selben nicht übereinstimme und mich von dem selben distanzierte, sollten darin Positionen, Haltungen, Bilder etc. enthalten sein, welche mit dem deutschen Recht nicht vereinbar sein sollten. 
