메뉴 건너뛰기




Volumn 78, Issue 5, 2016, Pages 947-1012

Causal inference by using invariant prediction: identification and confidence intervals

Author keywords

Causal discovery; Causal inference; Confidence intervals; Invariant prediction

Indexed keywords


EID: 84990890005     PISSN: 13697412     EISSN: 14679868     Source Type: Journal    
DOI: 10.1111/rssb.12167     Document Type: Article
Times cited : (973)

References (160)
  • 2
    • 0031531764 scopus 로고    scopus 로고
    • A characterization of Markov equivalence classes for acyclic digraphs
    • Andersson, S. A., Madigan, D. and Perlman, M. D. (1997) A characterization of Markov equivalence classes for acyclic digraphs. Ann. Statist., 25, 505–541.
    • (1997) Ann. Statist. , vol.25 , pp. 505-541
    • Andersson, S.A.1    Madigan, D.2    Perlman, M.D.3
  • 3
    • 0346880128 scopus 로고    scopus 로고
    • Identification of causal effects using instrumental variables
    • Angrist, J. D., Imbens, G. W. and Rubin, D. B. (1996) Identification of causal effects using instrumental variables. J. Am. Statist. Ass., 91, 444–455.
    • (1996) J. Am. Statist. Ass. , vol.91 , pp. 444-455
    • Angrist, J.D.1    Imbens, G.W.2    Rubin, D.B.3
  • 4
    • 82255196005 scopus 로고    scopus 로고
    • Square-root lasso: pivotal recovery of sparse signals via conic programming
    • Belloni, A., Chernozhukov, V. and Wang, L. (2011) Square-root lasso: pivotal recovery of sparse signals via conic programming. Biometrika, 98, 791–806.
    • (2011) Biometrika , vol.98 , pp. 791-806
    • Belloni, A.1    Chernozhukov, V.2    Wang, L.3
  • 8
    • 84987997394 scopus 로고    scopus 로고
    • CAM: causal additive models, high-dimensional order search and penalized regression
    • Bühlmann, P., Peters, J. and Ernest, J. (2014) CAM: causal additive models, high-dimensional order search and penalized regression. Ann. Statist., 42, 2526–2556.
    • (2014) Ann. Statist. , vol.42 , pp. 2526-2556
    • Bühlmann, P.1    Peters, J.2    Ernest, J.3
  • 9
    • 84886494144 scopus 로고    scopus 로고
    • Controlling false positive selections in high-dimensional regression and causal inference
    • Bühlmann, P., Rütimann, P. and Kalisch, M. (2013) Controlling false positive selections in high-dimensional regression and causal inference. Statist. Meth. Med. Res., 22, 466–492.
    • (2013) Statist. Meth. Med. Res. , vol.22 , pp. 466-492
    • Bühlmann, P.1    Rütimann, P.2    Kalisch, M.3
  • 10
    • 0043245810 scopus 로고    scopus 로고
    • Boosting with the L2-loss: regression and classification
    • Bühlmann, P. and Yu, B. (2003) Boosting with the L2-loss: regression and classification. J. Am. Statist. Ass., 98, 324–339.
    • (2003) J. Am. Statist. Ass. , vol.98 , pp. 324-339
    • Bühlmann, P.1    Yu, B.2
  • 11
    • 2542465947 scopus 로고    scopus 로고
    • On inclusion-driven learning of Bayesian networks
    • Castelo, R. and Kocka, T. (2003) On inclusion-driven learning of Bayesian networks. J. Mach. Learn. Res., 4, 527–574.
    • (2003) J. Mach. Learn. Res. , vol.4 , pp. 527-574
    • Castelo, R.1    Kocka, T.2
  • 12
    • 0042967741 scopus 로고    scopus 로고
    • Optimal structure identification with greedy search
    • Chickering, D. M. (2002) Optimal structure identification with greedy search. J. Mach. Learn. Res., 3, 507–554.
    • (2002) J. Mach. Learn. Res. , vol.3 , pp. 507-554
    • Chickering, D.M.1
  • 13
    • 0001369142 scopus 로고
    • Tests of equality between sets of coefficients in two linear regressions
    • Chow, G. C. (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591–605.
    • (1960) Econometrica , vol.28 , pp. 591-605
    • Chow, G.C.1
  • 14
    • 0007047929 scopus 로고    scopus 로고
    • Causal discovery from a mixture of experimental and observational data
    • In, San Francisco Morgan Kaufmann
    • Cooper, G. and Yoo, C. (1999) Causal discovery from a mixture of experimental and observational data. In Proc. 15th A. Conf. Uncertainty in Artificial Intelligence, pp. 116–125.San Francisco: Morgan Kaufmann.
    • (1999) Proc. 15th A. Conf. Uncertainty in Artificial Intelligence , pp. 116-125
    • Cooper, G.1    Yoo, C.2
  • 15
    • 0002614170 scopus 로고
    • Über eine Eigenschaft der normalen Verteilungsfunktion
    • Cramér, H. (1936) Über eine Eigenschaft der normalen Verteilungsfunktion. Math. Zeits., 41, 405–414.
    • (1936) Math. Zeits. , vol.41 , pp. 405-414
    • Cramér, H.1
  • 16
    • 1542742382 scopus 로고    scopus 로고
    • Causal inference without counterfactuals
    • Dawid, A. P. (2000) Causal inference without counterfactuals. J. Am. Statist. Ass., 95, 407–424.
    • (2000) J. Am. Statist. Ass. , vol.95 , pp. 407-424
    • Dawid, A.P.1
  • 18
    • 84957055317 scopus 로고    scopus 로고
    • The decision-theoretic approach to causal inference
    • In, (eds, C. R. Berzuini, A. P. Dawid, L. Bernardinelli,), ch. 4, Chichester, Wiley
    • Dawid, A. P. (2012) The decision-theoretic approach to causal inference. In Causality: Statistical Perspectives and Applications (eds C. R. Berzuini, A. P. Dawid and L. Bernardinelli), ch. 4, pp. 25–42. Chichester: Wiley
    • (2012) Causality: Statistical Perspectives and Applications , pp. 25-42
    • Dawid, A.P.1
  • 19
    • 84928163239 scopus 로고    scopus 로고
    • Statistical causality from a decision-theoretic perspective
    • Dawid, A. P. (2015) Statistical causality from a decision-theoretic perspective. A. Rev. Statist. Appl., 2, 273–303.
    • (2015) A. Rev. Statist. Appl. , vol.2 , pp. 273-303
    • Dawid, A.P.1
  • 20
    • 78751616989 scopus 로고    scopus 로고
    • Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview
    • Dawid, A. P. and Didelez, V. (2010) Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview. Statist. Surv., 4, 184–231.
    • (2010) Statist. Surv. , vol.4 , pp. 184-231
    • Dawid, A.P.1    Didelez, V.2
  • 21
    • 80053192470 scopus 로고    scopus 로고
    • Direct and indirect effects of sequential treatments
    • In, Corvallis Association for Uncertainty in Artificial Intelligence Press
    • Didelez, V., Dawid, A. P. and Geneletti, S. (2006) Direct and indirect effects of sequential treatments. In Proc. 22nd A. Conf. Uncertainty in Artifical Intelligence, pp. 138–146. Corvallis: Association for Uncertainty in Artificial Intelligence Press
    • (2006) Proc. 22nd A. Conf. Uncertainty in Artifical Intelligence , pp. 138-146
    • Didelez, V.1    Dawid, A.P.2    Geneletti, S.3
  • 22
    • 77957778209 scopus 로고    scopus 로고
    • Assumptions of IV methods for observational epidemiology
    • Didelez, V., Meng, S. and Sheehan, N. A. (2010) Assumptions of IV methods for observational epidemiology. Statist. Sci., 25, 22–40.
    • (2010) Statist. Sci. , vol.25 , pp. 22-40
    • Didelez, V.1    Meng, S.2    Sheehan, N.A.3
  • 24
    • 84885387466 scopus 로고    scopus 로고
    • Testing equality of functions under monotonicity constraints
    • Durot, C., Groeneboom, P. and Lopuhaä, H. (2013) Testing equality of functions under monotonicity constraints. J. Nonparam. Statist., 25, 939–970.
    • (2013) J. Nonparam. Statist. , vol.25 , pp. 939-970
    • Durot, C.1    Groeneboom, P.2    Lopuhaä, H.3
  • 26
    • 46649113945 scopus 로고    scopus 로고
    • Interventions and causal inference
    • Eberhardt, F. and Scheines, R. (2007) Interventions and causal inference. Philos. Sci., 74, 981–995.
    • (2007) Philos. Sci. , vol.74 , pp. 981-995
    • Eberhardt, F.1    Scheines, R.2
  • 27
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: a gradient boosting machine
    • Friedman, J. H. (2001) Greedy function approximation: a gradient boosting machine. Ann. Statist., 29, 1189–1232.
    • (2001) Ann. Statist. , vol.29 , pp. 1189-1232
    • Friedman, J.H.1
  • 28
    • 0032924944 scopus 로고    scopus 로고
    • Causal diagrams for epidemiologic research
    • Greenland, S., Pearl, J. and Robins, J. M. (1999) Causal diagrams for epidemiologic research. Epidemiology, 10, 37–48.
    • (1999) Epidemiology , vol.10 , pp. 37-48
    • Greenland, S.1    Pearl, J.2    Robins, J.M.3
  • 29
    • 0002556498 scopus 로고
    • The probability approach in econometrics
    • Haavelmo, T. (1944) The probability approach in econometrics. Econometrica, 12, suppl., S1–S115.
    • (1944) Econometrica , vol.12 , pp. S1-S115
    • Haavelmo, T.1
  • 30
    • 84869152656 scopus 로고    scopus 로고
    • Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs
    • Hauser, A. and Bühlmann, P. (2012) Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. J. Mach. Learn. Res., 13, 2409–2464.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 2409-2464
    • Hauser, A.1    Bühlmann, P.2
  • 31
    • 84917733558 scopus 로고    scopus 로고
    • Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs
    • Hauser, A. and Bühlmann, P. (2015) Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs. J. R. Statist. Soc. B, 77, 291–318.
    • (2015) J. R. Statist. Soc. B , vol.77 , pp. 291-318
    • Hauser, A.1    Bühlmann, P.2
  • 32
    • 57249084023 scopus 로고    scopus 로고
    • Active learning of causal networks with intervention experiments and optimal designs
    • He, Y.-B. and Geng, Z. (2008) Active learning of causal networks with intervention experiments and optimal designs. J. Mach. Learn. Res., 9, 2523–2547.
    • (2008) J. Mach. Learn. Res. , vol.9 , pp. 2523-2547
    • He, Y.-B.1    Geng, Z.2
  • 33
    • 33748106661 scopus 로고    scopus 로고
    • Instruments for causal inference: an epidemiologist's dream
    • Hernán, M. and Robins, J. (2006) Instruments for causal inference: an epidemiologist's dream? Epidemiology, 17, 360–372.
    • (2006) Epidemiology , vol.17 , pp. 360-372
    • Hernán, M.1    Robins, J.2
  • 34
    • 84971972604 scopus 로고
    • The logic of causal inference
    • Hoover, K. D. (1990) The logic of causal inference. Econ. Philos., 6, 207–234.
    • (1990) Econ. Philos. , vol.6 , pp. 207-234
    • Hoover, K.D.1
  • 37
    • 84870911885 scopus 로고    scopus 로고
    • Learning linear cyclic causal models with latent variables
    • Hyttinen, A., Eberhardt, F. and Hoyer, P. O. (2012) Learning linear cyclic causal models with latent variables. J. Mach. Learn. Res., 13, 3387–3439.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 3387-3439
    • Hyttinen, A.1    Eberhardt, F.2    Hoyer, P.O.3
  • 40
    • 33947524259 scopus 로고    scopus 로고
    • Estimating high-dimensional directed acyclic graphs with the PC-algorithm
    • Kalisch, M. and Bühlmann, P. (2007) Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res., 8, 613–636.
    • (2007) J. Mach. Learn. Res. , vol.8 , pp. 613-636
    • Kalisch, M.1    Bühlmann, P.2
  • 41
    • 84946954072 scopus 로고    scopus 로고
    • Instrumental variables estimation with some invalid instruments and its application to mendelian randomization
    • to be published
    • Kang, H., Zhang, A., Cai, T. and Small, D.S. (2015) Instrumental variables estimation with some invalid instruments and its application to mendelian randomization. J. Am. Statist. Ass., to be published.
    • (2015) J. Am. Statist. Ass.
    • Kang, H.1    Zhang, A.2    Cai, T.3    Small, D.S.4
  • 43
    • 33748994797 scopus 로고    scopus 로고
    • Evidence of off-target effects associated with long dsrnas in drosophila melanogaster cell-based assays
    • Kulkarni, M. M., Booker, M., Silver, S. J., Friedman, A., Hong, P., Perrimon, N. and Mathey-Prevot, B. (2006) Evidence of off-target effects associated with long dsrnas in drosophila melanogaster cell-based assays. Nat. Meth., 3, 833–838.
    • (2006) Nat. Meth. , vol.3 , pp. 833-838
    • Kulkarni, M.M.1    Booker, M.2    Silver, S.J.3    Friedman, A.4    Hong, P.5    Perrimon, N.6    Mathey-Prevot, B.7
  • 44
    • 0004047518 scopus 로고    scopus 로고
    • New York, Oxford University Press
    • Lauritzen, S. L. (1996) Graphical Models. New York: Oxford University Press.
    • (1996) Graphical Models
    • Lauritzen, S.L.1
  • 45
    • 0036420729 scopus 로고    scopus 로고
    • Chain graph models and their causal interpretations
    • Lauritzen, S. L. and Richardson, T. S. (2002) Chain graph models and their causal interpretations. J. R. Statist. Soc. B, 64, 321–348.
    • (2002) J. R. Statist. Soc. B , vol.64 , pp. 321-348
    • Lauritzen, S.L.1    Richardson, T.S.2
  • 46
    • 0001006209 scopus 로고
    • Local computations with probabilities on graphical structures and their application to expert systems (with discussion)
    • Lauritzen, S. L. and Spiegelhalter, D. J. (1988) Local computations with probabilities on graphical structures and their application to expert systems (with discussion). J. R. Statist. Soc. B, 50, 157–224.
    • (1988) J. R. Statist. Soc. B , vol.50 , pp. 157-224
    • Lauritzen, S.L.1    Spiegelhalter, D.J.2
  • 47
    • 69949166983 scopus 로고    scopus 로고
    • Estimating high-dimensional intervention effects from observational data
    • Maathuis, M., Kalisch, M. and Bühlmann, P. (2009) Estimating high-dimensional intervention effects from observational data. Ann. Statist., 37, 3133–3164.
    • (2009) Ann. Statist. , vol.37 , pp. 3133-3164
    • Maathuis, M.1    Kalisch, M.2    Bühlmann, P.3
  • 50
    • 84897585225 scopus 로고    scopus 로고
    • Identifiability of Gaussian structural equation models with equal error variances
    • Peters, J. and Bühlmann, P. (2014) Identifiability of Gaussian structural equation models with equal error variances. Biometrika, 101, 219–228.
    • (2014) Biometrika , vol.101 , pp. 219-228
    • Peters, J.1    Bühlmann, P.2
  • 54
    • 0036392228 scopus 로고    scopus 로고
    • Ancestral graph markov models
    • Richardson, T. and Spirtes, P. (2002) Ancestral graph markov models. Ann. Statist., 30, 962–1030.
    • (2002) Ann. Statist. , vol.30 , pp. 962-1030
    • Richardson, T.1    Spirtes, P.2
  • 55
    • 46149139403 scopus 로고
    • A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
    • Robins, J. M. (1986) A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Math. Modllng, 7, 1393–1512.
    • (1986) Math. Modllng , vol.7 , pp. 1393-1512
    • Robins, J.M.1
  • 56
    • 0033847784 scopus 로고    scopus 로고
    • Marginal structural models and causal inference in epidemiology
    • Robins, J. M., Hernan, M. A. and Brumback, B. (2000) Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550–560.
    • (2000) Epidemiology , vol.11 , pp. 550-560
    • Robins, J.M.1    Hernan, M.A.2    Brumback, B.3
  • 58
    • 21844508577 scopus 로고
    • Democratization or diversion?: The effect of community colleges on educational attainment
    • Rouse, C. E. (1995) Democratization or diversion?: The effect of community colleges on educational attainment. J. Bus. Econ. Statist., 13, 217–224.
    • (1995) J. Bus. Econ. Statist. , vol.13 , pp. 217-224
    • Rouse, C.E.1
  • 59
    • 14944344423 scopus 로고    scopus 로고
    • Causal inference using potential outcomes
    • Rubin, D. B. (2005) Causal inference using potential outcomes. J. Am. Statist. Ass., 100, 322–331.
    • (2005) J. Am. Statist. Ass. , vol.100 , pp. 322-331
    • Rubin, D.B.1
  • 60
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: a new explanation for the effectiveness of voting methods
    • Schapire, R. E., Freund, Y., Bartlett, P. and Lee, W. S. (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Statist., 26, 1651–1686.
    • (1998) Ann. Statist. , vol.26 , pp. 1651-1686
    • Schapire, R.E.1    Freund, Y.2    Bartlett, P.3    Lee, W.S.4
  • 66
    • 43549116852 scopus 로고    scopus 로고
    • Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling
    • Terza, J., Basu, A. and Rathouz, P. (2008) Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling. J. Hlth Econ., 27, 531–543.
    • (2008) J. Hlth Econ. , vol.27 , pp. 531-543
    • Terza, J.1    Basu, A.2    Rathouz, P.3
  • 68
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B, 58, 267–288.
    • (1996) J. R. Statist. Soc. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 69
    • 74049158300 scopus 로고    scopus 로고
    • Signed directed acyclic graphs for causal inference
    • VanderWeele, T. J. and Robins, J. M. (2010) Signed directed acyclic graphs for causal inference. J. R. Statist. Soc. B, 72, 111–127.
    • (2010) J. R. Statist. Soc. B , vol.72 , pp. 111-127
    • VanderWeele, T.J.1    Robins, J.M.2
  • 72
    • 0001589778 scopus 로고
    • Correlation and causation
    • Wright, S. (1921) Correlation and causation. J. Agric. Res., 20, 557–585.
    • (1921) J. Agric. Res. , vol.20 , pp. 557-585
    • Wright, S.1
  • 75
    • 0002501316 scopus 로고    scopus 로고
    • An algorithm for finding minimum d-seperating sets in belief networks
    • In, eds, F. V. Jensen, E. Horvitz, San Francisco, Morgan Kaufmann
    • AcidS.de CamposL. M. 1996An algorithm for finding minimum d-seperating sets in belief networks In Proc. 12th A. Conf. Uncertainty in Artificial Intelligence eds F. V. JensenE. Horvitz pp 310San FranciscoMorgan Kaufmann
    • (1996) Proc. 12th A. Conf. Uncertainty in Artificial Intelligence , pp. 3-10
    • Acid, S.1    de Campos, L.M.2
  • 77
    • 69949186291 scopus 로고    scopus 로고
    • Identifiability of parameters in latent structure models with many observed variables
    • Allman E. Matias C. Rhodes J. 2009 Identifiability of parameters in latent structure models with many observed variables Ann. Statist. 6 3009 3132
    • (2009) Ann. Statist. , vol.6 , pp. 3009-3132
    • Allman, E.1    Matias, C.2    Rhodes, J.3
  • 78
  • 79
    • 84862236788 scopus 로고    scopus 로고
    • Local characterizations of causal Bayesian networks
    • (eds M. Croituru, S. Rudolph, N. Wilson, J. Howse and O. Corby) pp, Berlin Springer
    • BareinboimE.BritoC.PearlJ. 2012Local characterizations of causal Bayesian networks In Graph Structures for Knowledge Representation and Reasoning (eds M. Croituru, S. Rudolph, N. Wilson, J. Howse and O. Corby) pp 117 Berlin: Springer
    • (2012) Graph Structures for Knowledge Representation and Reasoning , pp. 1-17
    • Bareinboim, E.1    Brito, C.2    Pearl, J.3
  • 80
    • 84977267946 scopus 로고    scopus 로고
    • Causal inference and the data-fusion problem
    • Bareinboim E. Pearl J. 2016 Causal inference and the data-fusion problem Proc. Natn. Acad. Sci. USA 113 7345 7352
    • (2016) Proc. Natn. Acad. Sci. USA , vol.113 , pp. 7345-7352
    • Bareinboim, E.1    Pearl, J.2
  • 82
    • 0000245743 scopus 로고    scopus 로고
    • Statistical modeling: the two cultures (with comments)
    • Breiman L. 2001 Statistical modeling: the two cultures (with comments) Statist. Sci. 16 199 231
    • (2001) Statist. Sci. , vol.16 , pp. 199-231
    • Breiman, L.1
  • 84
    • 84867677322 scopus 로고    scopus 로고
    • Learning high-dimensional directed acyclic graphs with latent and selection variables
    • Colombo D. Maathuis M. H. Kalisch M. Richardson T. S. 2012 Learning high-dimensional directed acyclic graphs with latent and selection variables Ann. Statist. 40 294 321
    • (2012) Ann. Statist. , vol.40 , pp. 294-321
    • Colombo, D.1    Maathuis, M.H.2    Kalisch, M.3    Richardson, T.S.4
  • 86
    • 21944436304 scopus 로고    scopus 로고
    • A simple constraint-based algorithm for efficiently mining observational databases for causal relationships
    • Cooper G. F. 1997 A simple constraint-based algorithm for efficiently mining observational databases for causal relationships Data Minng Knowl. Discov. 1 203 224
    • (1997) Data Minng Knowl. Discov. , vol.1 , pp. 203-224
    • Cooper, G.F.1
  • 88
    • 1542742382 scopus 로고    scopus 로고
    • Causal inference without counterfactuals (with discussion)
    • Dawid A. P. 2000 Causal inference without counterfactuals (with discussion) J. Am. Statist. Ass. 95 407 448
    • (2000) J. Am. Statist. Ass. , vol.95 , pp. 407-448
    • Dawid, A.P.1
  • 89
    • 0036679398 scopus 로고    scopus 로고
    • Influence diagrams for causal modelling and inference
    • Dawid A. P. 2002 Influence diagrams for causal modelling and inference Int. Statist. Rev. 70 161 189
    • (2002) Int. Statist. Rev. , vol.70 , pp. 161-189
    • Dawid, A.P.1
  • 90
    • 84928163239 scopus 로고    scopus 로고
    • Statistical causality from a decision-theoretic perspective
    • Dawid A. P. 2015 Statistical causality from a decision-theoretic perspective A. Rev. Statist. Appl. 2 273 303
    • (2015) A. Rev. Statist. Appl. , vol.2 , pp. 273-303
    • Dawid, A.P.1
  • 91
    • 78751616989 scopus 로고    scopus 로고
    • Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview
    • Dawid A. P. Didelez V. 2010 Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview Statist. Surv. 4 184 231
    • (2010) Statist. Surv. , vol.4 , pp. 184-231
    • Dawid, A.P.1    Didelez, V.2
  • 92
  • 93
    • 84862969866 scopus 로고    scopus 로고
    • Identifiability and estimation of causal effects by principal stratification with outcomes truncated by death
    • Ding P. Geng Z. Yan W. Zhou X. 2011 Identifiability and estimation of causal effects by principal stratification with outcomes truncated by death J. Am. Statist. Ass. 106 1578 159l
    • (2011) J. Am. Statist. Ass. , vol.106 , pp. 1578-1591
    • Ding, P.1    Geng, Z.2    Yan, W.3    Zhou, X.4
  • 95
    • 49549100459 scopus 로고    scopus 로고
    • Learning causal Bayesian network structures from experimental data
    • Ellis B. Wong W. H. 2008 Learning causal Bayesian network structures from experimental data J. Am. Statist. Ass. 103 778 789
    • (2008) J. Am. Statist. Ass. , vol.103 , pp. 778-789
    • Ellis, B.1    Wong, W.H.2
  • 96
    • 84991019291 scopus 로고    scopus 로고
    • Causal inference
    • In, Encyclopedia Britannica
    • Encyclopedia Britannica 2014Causal inference In Encyclopedia Britannica. Encyclopedia Britannica.
    • (2014) Encyclopedia Britannica
  • 97
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its oracle properties
    • Fan J. Li R. 2001 Variable selection via nonconcave penalized likelihood and its oracle properties J. Am. Statist. Ass. 96 1348 1360
    • (2001) J. Am. Statist. Ass. , vol.96 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 98
    • 53849086824 scopus 로고    scopus 로고
    • Sure independence screening for ultrahigh dimensional feature space (with discussion)
    • FanJ.LvJ. 2008Sure independence screening for ultrahigh dimensional feature space (with discussion)J. R. Statist. Soc. B 70849911
    • (2008) J. R. Statist. Soc. B , vol.70 , pp. 849-911
    • Fan, J.1    Lv, J.2
  • 99
    • 84891503206 scopus 로고    scopus 로고
    • Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: an application to single cell data
    • Finkenstädt B. Woodcock D. J. Komorowski M. Harper C. V. Davis J. R. E. White M. R. H. Rand D. A. 2013 Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: an application to single cell data Ann. Appl. Statist. 7 1960 1982
    • (2013) Ann. Appl. Statist. , vol.7 , pp. 1960-1982
    • Finkenstädt, B.1    Woodcock, D.J.2    Komorowski, M.3    Harper, C.V.4    Davis, J.R.E.5    White, M.R.H.6    Rand, D.A.7
  • 100
    • 84990937530 scopus 로고    scopus 로고
    • Building” exact confidence nets
    • to be published
    • FrancisA.StehlíkM.WynnH. 2016“Building” exact confidence netsBernoulli, to be published.
    • (2016) Bernoulli
    • Francis, A.1    Stehlík, M.2    Wynn, H.3
  • 101
    • 17044423540 scopus 로고    scopus 로고
    • Are there algorithms that discover causal structure
    • Freedman D. Humphreys P. 1999 Are there algorithms that discover causal structure Synthese 121 29 54
    • (1999) Synthese , vol.121 , pp. 29-54
    • Freedman, D.1    Humphreys, P.2
  • 102
    • 84988001472 scopus 로고    scopus 로고
    • On asymptotically optimal confidence regions and tests for high-dimensional models
    • van de Geer S. Bühlmann P. Ritov Y. Dezeure R. 2014 On asymptotically optimal confidence regions and tests for high-dimensional models Ann. Statist. 42 1166 1202
    • (2014) Ann. Statist. , vol.42 , pp. 1166-1202
    • van de Geer, S.1    Bühlmann, P.2    Ritov, Y.3    Dezeure, R.4
  • 103
    • 0000351727 scopus 로고
    • Investigating causal relations by econometric models and cross-spectral methods
    • Granger C. W. J. 1969 Investigating causal relations by econometric models and cross-spectral methods Econometrica 137 424 438
    • (1969) Econometrica , vol.137 , pp. 424-438
    • Granger, C.W.J.1
  • 104
    • 84907479914 scopus 로고
    • eds, D. F. Hendry, M. S. Morgan, Cambridge, Cambridge University Press
    • HaavelmoT. 1995The Foundations of Econometric Analysis eds D. F. HendryM. S. Morgan pp 440453CambridgeCambridge University Press
    • (1995) The Foundations of Econometric Analysis , pp. 440-453
    • Haavelmo, T.1
  • 108
    • 21644434287 scopus 로고
    • Fiducial theory and invariant prediction
    • Hora R. B. Buehler R. J. 1967 Fiducial theory and invariant prediction Ann. Math. Statist. 38 795 801
    • (1967) Ann. Math. Statist. , vol.38 , pp. 795-801
    • Hora, R.B.1    Buehler, R.J.2
  • 110
    • 84959499555 scopus 로고    scopus 로고
    • Analysis of air quality time series of Hong Kong with graphical modeling
    • Hu F. Lu Z. Wong H. Yuen T. P. 2016 Analysis of air quality time series of Hong Kong with graphical modeling Environmetrics 27 169 181
    • (2016) Environmetrics , vol.27 , pp. 169-181
    • Hu, F.1    Lu, Z.2    Wong, H.3    Yuen, T.P.4
  • 112
    • 0002416976 scopus 로고
    • Normal multivariate analysis and the orthogonal group
    • James A. T. 1954 Normal multivariate analysis and the orthogonal group Ann. Math. Statist. 25 40 75
    • (1954) Ann. Math. Statist. , vol.25 , pp. 40-75
    • James, A.T.1
  • 113
    • 84947998178 scopus 로고    scopus 로고
    • Principal causal effect identification and principal surrogate end point evaluation by multiple trials
    • JiangZ.DingP.GengZ. 2016Principal causal effect identification and principal surrogate end point evaluation by multiple trialsJ. R. Statist. Soc. B 79829848
    • (2016) J. R. Statist. Soc. B , vol.79 , pp. 829-848
    • Jiang, Z.1    Ding, P.2    Geng, Z.3
  • 114
    • 0036960228 scopus 로고    scopus 로고
    • Estimation of intervention effects with noncompliance: alternative model specifications
    • Jo B. 2002 Estimation of intervention effects with noncompliance: alternative model specifications J. Educ. Behav. Statist. 27 385 409
    • (2002) J. Educ. Behav. Statist. , vol.27 , pp. 385-409
    • Jo, B.1
  • 115
    • 0000955861 scopus 로고
    • Exponential dispersion models (with discussion)
    • JørgensenB. 1987Exponential dispersion models (with discussion)J. R. Statist. Soc. B 49127162
    • (1987) J. R. Statist. Soc. B , vol.49 , pp. 127-162
    • Jørgensen, B.1
  • 116
    • 33947524259 scopus 로고    scopus 로고
    • Estimating high-dimensional directed acyclic graphs with the PC-algorithm
    • Kalisch M. Bühlmann P. 2007 Estimating high-dimensional directed acyclic graphs with the PC-algorithm J. Mach. Learn. Res. 8 613 636
    • (2007) J. Mach. Learn. Res. , vol.8 , pp. 613-636
    • Kalisch, M.1    Bühlmann, P.2
  • 118
    • 33846651400 scopus 로고    scopus 로고
    • Experimental analysis of neighborhood effects
    • Kling J. R. Liebman J. B. Katz L. F. 2007 Experimental analysis of neighborhood effects Econometrica 75 83 119
    • (2007) Econometrica , vol.75 , pp. 83-119
    • Kling, J.R.1    Liebman, J.B.2    Katz, L.F.3
  • 119
    • 0001926461 scopus 로고    scopus 로고
    • Causal inference from graphical models
    • In, eds, O. E. Barndorff-Nielsen, D. R. Cox and C. Kl, ppelberg, Boca Raton, Chapman and Hall–CRC
    • LauritzenS. L. 2001Causal inference from graphical models In Complex Stochastic Systems eds O. E. Barndorff-Nielsen D. R. Cox and C. Klu¨ ppelberg Boca RatonChapman and Hall–CRC
    • (2001) Complex Stochastic Systems
    • Lauritzen, S.L.1
  • 120
    • 0035466786 scopus 로고    scopus 로고
    • Representing and solving decision problems with limited information
    • Lauritzen S. L. Nilsson D. 2001 Representing and solving decision problems with limited information Mangmnt Sci. 47 1235 1251
    • (2001) Mangmnt Sci. , vol.47 , pp. 1235-1251
    • Lauritzen, S.L.1    Nilsson, D.2
  • 121
    • 84871997442 scopus 로고    scopus 로고
    • Functional causal mediation analysis with an application to brain connectivity
    • Lindquist M. A. 2012 Functional causal mediation analysis with an application to brain connectivity J. Am. Statist. Ass. 107 1297 1309
    • (2012) J. Am. Statist. Ass. , vol.107 , pp. 1297-1309
    • Lindquist, M.A.1
  • 122
    • 84866688958 scopus 로고    scopus 로고
    • Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data
    • Luo R. Zhao H. 2011 Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data Ann. Appl. Statist. 5 725 745
    • (2011) Ann. Appl. Statist. , vol.5 , pp. 725-745
    • Luo, R.1    Zhao, H.2
  • 123
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the lasso
    • Meinshausen N. Bühlmann P. 2006 High-dimensional graphs and variable selection with the lasso Ann. Statist. 34 1436 1462
    • (2006) Ann. Statist. , vol.34 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 126
    • 0002324387 scopus 로고
    • Semiparametric efficiency bounds
    • Newey W. K. 1990 Semiparametric efficiency bounds J. Appl. Econmetr. 5 99 135
    • (1990) J. Appl. Econmetr. , vol.5 , pp. 99-135
    • Newey, W.K.1
  • 127
    • 84866453034 scopus 로고    scopus 로고
    • Network inference using steady state data and Goldbeter–Koshland kinetics
    • Oates C. J. Hennessy B. T. Lu Y. Mills G. B. Mukherjee S. 2012 Network inference using steady state data and Goldbeter–Koshland kinetics Bioinformatics 28 2342 2348
    • (2012) Bioinformatics , vol.28 , pp. 2342-2348
    • Oates, C.J.1    Hennessy, B.T.2    Lu, Y.3    Mills, G.B.4    Mukherjee, S.5
  • 130
    • 84866463900 scopus 로고    scopus 로고
    • Network inference and biological dynamics
    • Oates C. J. Mukherjee S. 2012 Network inference and biological dynamics Ann. Appl. Statist. 6 1209 1235
    • (2012) Ann. Appl. Statist. , vol.6 , pp. 1209-1235
    • Oates, C.J.1    Mukherjee, S.2
  • 131
    • 65349149590 scopus 로고
    • Multivariate procedures invariant under linear transformations
    • Obenchein R. L. 1971 Multivariate procedures invariant under linear transformations Ann. Math. Statist. 42 1569 1578
    • (1971) Ann. Math. Statist. , vol.42 , pp. 1569-1578
    • Obenchein, R.L.1
  • 133
    • 77649325496 scopus 로고    scopus 로고
    • Causal inference in statistics: an overview
    • Pearl J. 2009 Causal inference in statistics: an overview Statist. Surv. 3 96 146
    • (2009) Statist. Surv. , vol.3 , pp. 96-146
    • Pearl, J.1
  • 135
    • 84921464994 scopus 로고    scopus 로고
    • External validity: from do-calculus to transportability across populations
    • Pearl J. Bareinboim E. 2014 External validity: from do-calculus to transportability across populations Statist. Sci. 29 579 595
    • (2014) Statist. Sci. , vol.29 , pp. 579-595
    • Pearl, J.1    Bareinboim, E.2
  • 137
    • 84959105170 scopus 로고    scopus 로고
    • A two-sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis
    • Pomann G.-M. Staicu A.-M. Ghosh S. 2016 A two-sample distribution-free test for functional data with application to a diffusion tensor imaging study of multiple sclerosis Appl. Statist. 65 395 414
    • (2016) Appl. Statist. , vol.65 , pp. 395-414
    • Pomann, G.-M.1    Staicu, A.-M.2    Ghosh, S.3
  • 138
    • 84883028224 scopus 로고    scopus 로고
    • Under what assumptions do site-by-treatment instruments identify average causal effects?
    • Reardon S. F. Raudenbush S. W. 2013 Under what assumptions do site-by-treatment instruments identify average causal effects? Sociol. Meth. Res. 42 143 163
    • (2013) Sociol. Meth. Res. , vol.42 , pp. 143-163
    • Reardon, S.F.1    Raudenbush, S.W.2
  • 139
    • 0040630191 scopus 로고    scopus 로고
    • A discovery algorithm for directed cyclic graphs
    • In, eds, F. V. Jensen, E. Horvitz, San Francisco, Morgan Kaufmann
    • RichardsonT. S. 1996A discovery algorithm for directed cyclic graphs In Proc. 12th A. Conf. Uncertainty in Artificial Intelligenceeds F. V. JensenE. Horvitz pp 454461San FranciscoMorgan Kaufmann
    • (1996) Proc. 12th A. Conf. Uncertainty in Artificial Intelligence , pp. 454-461
    • Richardson, T.S.1
  • 140
    • 0036392228 scopus 로고    scopus 로고
    • Ancestral graph Markov models
    • Richardson T. Spirtes P. 2002 Ancestral graph Markov models Ann. Statist. 30 962 1030
    • (2002) Ann. Statist. , vol.30 , pp. 962-1030
    • Richardson, T.1    Spirtes, P.2
  • 141
    • 0001015983 scopus 로고
    • Correcting for non-compliance in randomized trials using rank preserving structural failure time models
    • Robins J. M. Tsiatis A. A. 1991 Correcting for non-compliance in randomized trials using rank preserving structural failure time models Communs Statist. Theor. Meth. 20 2609 2631
    • (1991) Communs Statist. Theor. Meth. , vol.20 , pp. 2609-2631
    • Robins, J.M.1    Tsiatis, A.A.2
  • 143
    • 84908280913 scopus 로고    scopus 로고
    • Counterfactual, analyses with graphical models based on local independence
    • Røysland K. 2012 Counterfactual, analyses with graphical models based on local independence Ann. Statist. 40 2162 2194
    • (2012) Ann. Statist. , vol.40 , pp. 2162-2194
    • Røysland, K.1
  • 144
    • 0002531157 scopus 로고
    • Bayesian inference for causal effects: the role of randomization
    • Rubin D. B. 1978 Bayesian inference for causal effects: the role of randomization Ann. Statist. 6 34 58
    • (1978) Ann. Statist. , vol.6 , pp. 34-58
    • Rubin, D.B.1
  • 145
    • 17644427718 scopus 로고    scopus 로고
    • Causal protein-signaling networks derived from multiparameter single-cell data
    • Sachs K. Perez O. Pe'er D. Lauffenburger D. A. Nolan G. P. 2005 Causal protein-signaling networks derived from multiparameter single-cell data Science 308 523 529
    • (2005) Science , vol.308 , pp. 523-529
    • Sachs, K.1    Perez, O.2    Pe'er, D.3    Lauffenburger, D.A.4    Nolan, G.P.5
  • 151
    • 84908285303 scopus 로고    scopus 로고
    • Causal interpretation of stochastic differential equations
    • Sokol A. Hansen N. R. 2011 Causal interpretation of stochastic differential equations Electron. J. Probab. 19 1 24
    • (2011) Electron. J. Probab. , vol.19 , pp. 1-24
    • Sokol, A.1    Hansen, N.R.2
  • 152
    • 0000600340 scopus 로고
    • General intelligence,” objectively determined and measured
    • Spearman C. 1904 “General intelligence,” objectively determined and measured Am. J. Psychol. 15 210 293
    • (1904) Am. J. Psychol. , vol.15 , pp. 210-293
    • Spearman, C.1
  • 155
    • 84884906891 scopus 로고    scopus 로고
    • Causal identifiability via chain event graphs
    • Thwaites P. A. 2013 Causal identifiability via chain event graphs Artif. Intell. 195 291 315
    • (2013) Artif. Intell. , vol.195 , pp. 291-315
    • Thwaites, P.A.1
  • 158
    • 84891502322 scopus 로고    scopus 로고
    • Causal inference under multiple versions of treatment
    • VanderWeele T. Hernan M. 2013 Causal inference under multiple versions of treatment J. Causl Inf. 1 1 20
    • (2013) J. Causl Inf. , vol.1 , pp. 1-20
    • VanderWeele, T.1    Hernan, M.2
  • 159
    • 84991000367 scopus 로고    scopus 로고
    • Granger causality
    • In, (Available from
    • Wikipedia 2016Granger causality In Wikipedia. (Available from https://en.wikipedia.org/wiki/Granger__causality.)
    • (2016) Wikipedia
  • 160
    • 7244261743 scopus 로고    scopus 로고
    • Causal linkages among Shanghai, Shenzhen, and Hong Kong stock markets
    • Zhu H. Lu Z. Wang S. Soofi A. S. 2004 Causal linkages among Shanghai, Shenzhen, and Hong Kong stock markets Int. J. Theoret. Appl. Finan. 7 135 149
    • (2004) Int. J. Theoret. Appl. Finan. , vol.7 , pp. 135-149
    • Zhu, H.1    Lu, Z.2    Wang, S.3    Soofi, A.S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.