IEEE Trans Pattern Anal Mach Intell - Causal Inference on Discrete Data using Additive Noise Models.

Tópicos

{ model(3404) distribut(989) bayesian(671) }
{ studi(1119) effect(1106) posit(819) }
{ estim(2440) model(1874) function(577) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(2212) result(1239) propos(1039) }
{ can(774) often(719) complex(702) }
{ imag(2830) propos(1344) filter(1198) }
{ general(901) number(790) one(736) }
{ method(1557) propos(1049) approach(1037) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ sampl(1606) size(1419) use(1276) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ use(1733) differ(960) four(931) }
{ sequenc(1873) structur(1644) protein(1328) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ blood(1257) pressur(1144) flow(957) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(981) present(881) function(850) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
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{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
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{ network(2748) neural(1063) input(814) }
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{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ surgeri(1148) surgic(1085) robot(1054) }
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{ clinic(1479) use(1117) guidelin(835) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
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{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
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{ import(1318) role(1303) understand(862) }
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{ compound(1573) activ(1297) structur(1058) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. The case of two random variables is particularly challenging since no (conditional) independences can be exploited. Recent methods that are based on additive noise models suggest the following principle: Whenever the joint distribution P^(X,Y) admits such a model in one direction, e.g. Y=f(X)+N, N independent of X, but does not admit the reversed model X=g(Y)+Ñ, Ñ independent of Y, one infers the former direction to be causal (i.e. X-→Y). Up to know these approaches only deal with 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 it almost never occurs that additive noise models can be fit in both directions. We further propose an efficient algorithm that is able to perform this way of causal inference on finite samples of discrete variables. We show that the algorithm works both on synthetic and real data sets.

Resumo Limpo

infer causal structur set random variabl finit sampl joint distribut import problem scienc case two random variabl particular challeng sinc condit independ can exploit recent method base addit nois model suggest follow principl whenev joint distribut pxi admit model one direct eg yfxn n independ x admit revers model xgyxd xd independ y one infer former direct causal ie xxi know approach deal continu variabl mani situat howev variabl interest discret even finit mani state work extend notion addit nois model case prove almost never occur addit nois model can fit direct propos effici algorithm abl perform way causal infer finit sampl discret variabl show algorithm work synthet real data set

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