-
1
-
-
33644851650
-
Doubly robust estimation in missing data and causal inference models
-
Bang, H. and Robins, J. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61:962–972.
-
(2005)
Biometrics
, vol.61
, pp. 962-972
-
-
Bang, H.1
Robins, J.2
-
4
-
-
41549141939
-
Boosting algorithms: Regularization, prediction and model fitting (with discussion)
-
Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: regularization, prediction and model fitting (with discussion). Statistical Science, 22:477–505.
-
(2007)
Statistical Science
, vol.22
, pp. 477-505
-
-
Bühlmann, P.1
Hothorn, T.2
-
5
-
-
84987997394
-
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. Annals of Statistics, 42:2526–2556.
-
(2014)
Annals of Statistics
, vol.42
, pp. 2526-2556
-
-
Bühlmann, P.1
Peters, J.2
Ernest, J.3
-
7
-
-
0042967741
-
Optimal structure identification with greedy search
-
Chickering, D. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3:507–554.
-
(2002)
Journal of Machine Learning Research
, vol.3
, pp. 507-554
-
-
Chickering, D.1
-
8
-
-
84867677322
-
Learning high-dimensional directed acyclic graphs with latent and selection variables
-
Colombo, D., Maathuis, M., Kalisch, M., and Richardson, T. (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables. Annals of Statistics, 40:294–321.
-
(2012)
Annals of Statistics
, vol.40
, pp. 294-321
-
-
Colombo, D.1
Maathuis, M.2
Kalisch, M.3
Richardson, T.4
-
10
-
-
77951643877
-
Cause and effect
-
Editorial
-
Editorial (2010). Cause and effect. Nature Methods, 7:243.
-
(2010)
Nature Methods
, vol.7
, pp. 243
-
-
-
11
-
-
0032359619
-
-
Fan, J., Härdle, W., and Mammen, E. (1998). Direct estimation of lowdimensional components in additive models. Annals of Statistics, 26:943–971.
-
(1998)
Direct Estimation of Lowdimensional Components in Additive Models. Annals of Statistics
, vol.26
, pp. 943-971
-
-
Fan, J.1
Härdle, W.2
Mammen, E.3
-
12
-
-
0035470889
-
Greedy function approximation: A gradient boosting machine
-
Friedman, J. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29:1189–1232.
-
(2001)
Annals of Statistics
, vol.29
, pp. 1189-1232
-
-
Friedman, J.1
-
13
-
-
0842288337
-
Inferring cellular networks using probabilistic graphical models
-
Friedman, N. (2004). Inferring cellular networks using probabilistic graphical models. Science, 303:799–805.
-
(2004)
Science
, vol.303
, pp. 799-805
-
-
Friedman, N.1
-
14
-
-
0032924944
-
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
-
15
-
-
38249035197
-
Estimation of integrated squared density derivatives
-
Hall, P. and Marron, J. (1987). Estimation of integrated squared density derivatives. Statistics & Probability Letters, 6:109–115.
-
(1987)
Statistics & Probability Letters
, vol.6
, pp. 109-115
-
-
Hall, P.1
Marron, J.2
-
16
-
-
0003841907
-
-
John Wiley & Sons
-
Hampel, F., Ronchetti, E., Rousseeuw, P., and Stahel, W. (2011). Robust statistics: the approach based on influence functions. John Wiley & Sons.
-
(2011)
Robust Statistics: The Approach Based on Influence Functions
-
-
Hampel, F.1
Ronchetti, E.2
Rousseeuw, P.3
Stahel, W.4
-
17
-
-
84869152656
-
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. The Journal of Machine Learning Research, 13:2409–2464.
-
(2012)
The Journal of Machine Learning Research
, vol.13
, pp. 2409-2464
-
-
Hauser, A.1
Bühlmann, P.2
-
18
-
-
84896548173
-
Two optimal strategies for active learning of causal models from interventional data
-
Hauser, A. and Bühlmann, P. (2014). Two optimal strategies for active learning of causal models from interventional data. International Journal of Approximate Reasoning, 55:926–939.
-
(2014)
International Journal of Approximate Reasoning
, vol.55
, pp. 926-939
-
-
Hauser, A.1
Bühlmann, P.2
-
19
-
-
84917733558
-
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. Journal of the Royal Statistical Society, Series B, 77:291–318.
-
(2015)
Journal of the Royal Statistical Society, Series B
, vol.77
, pp. 291-318
-
-
Hauser, A.1
Bühlmann, P.2
-
20
-
-
57249084023
-
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. Journal of Machine Learning Research, 9:2523–2547.
-
(2008)
Journal of Machine Learning Research
, vol.9
, pp. 2523-2547
-
-
He, Y.-B.1
Geng, Z.2
-
21
-
-
33847398253
-
Optimal estimation in additive regression models
-
Horowitz, J., Klemelä, J., and Mammen, E. (2006). Optimal estimation in additive regression models. Bernoulli, 12:271–298.
-
(2006)
Bernoulli
, vol.12
, pp. 271-298
-
-
Horowitz, J.1
Klemelä, J.2
Mammen, E.3
-
22
-
-
84858789485
-
Nonlinear causal discovery with additive noise models. In
-
Hoyer, P., Janzing, D., Mooij, J., Peters, J., and Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. In Advances in Neural Information Processing Systems 21, 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008), pages 689–696.
-
(2009)
Advances in Neural Information Processing Systems 21, 22Nd Annual Conference on Neural Information Processing Systems (NIPS 2008)
, pp. 689-696
-
-
Hoyer, P.1
Janzing, D.2
Mooij, J.3
Peters, J.4
Schölkopf, B.5
-
23
-
-
0344464762
-
Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks
-
Husmeier, D. (2003). Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics, 19:2271–2282.
-
(2003)
Bioinformatics
, vol.19
, pp. 2271-2282
-
-
Husmeier, D.1
-
24
-
-
0036372453
-
Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression
-
Imoto, S., Goto, T., and Miyano, S. (2002). Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. In Proceedings of the Pacific Symposium on Biocomputing (PSB-2002), volume 7, pages 175–186.
-
(2002)
Proceedings of the Pacific Symposium on Biocomputing (PSB-2002)
, vol.7
, pp. 175-186
-
-
Imoto, S.1
Goto, T.2
Miyano, S.3
-
25
-
-
33947524259
-
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. Journal of Machine Learning Research, 8:613–636.
-
(2007)
Journal of Machine Learning Research
, vol.8
, pp. 613-636
-
-
Kalisch, M.1
Bühlmann, P.2
-
27
-
-
0001006209
-
Local computations with probabilities on graphical structures and their application to expert systems
-
Lauritzen, S. and Spiegelhalter, D. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B, 50:157–224.
-
(1988)
Journal of the Royal Statistical Society, Series B
, vol.50
, pp. 157-224
-
-
Lauritzen, S.1
Spiegelhalter, D.2
-
28
-
-
79955126371
-
Higher order inference on a treatment effect under low regularity conditions
-
Li, L., Tchetgen, E. T., van der Vaart, A., and Robins, J. (2011). Higher order inference on a treatment effect under low regularity conditions. Statistics & Probability Letters, 81:821–828.
-
(2011)
Statistics & Probability Letters
, vol.81
, pp. 821-828
-
-
Li, L.1
Tchetgen, E.T.2
Van Der Vaart, A.3
Robins, J.4
-
29
-
-
77956888636
-
A kernel method of estimating structured nonparametric regression based on marginal integration
-
Linton, O. and Nielsen, J. P. (1995). A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika, 82:93–100.
-
(1995)
Biometrika
, vol.82
, pp. 93-100
-
-
Linton, O.1
Nielsen, J.P.2
-
30
-
-
84919742315
-
High-dimensional learning of linear causal networks via inverse covariance estimation
-
Loh, P. and Bühlmann, P. (2014). High-dimensional learning of linear causal networks via inverse covariance estimation. Journal of Machine Learning Research, 15:3065–3105.
-
(2014)
Journal of Machine Learning Research
, vol.15
, pp. 3065-3105
-
-
Loh, P.1
Bühlmann, P.2
-
31
-
-
77951641247
-
Predicting causal effects in large-scale systems from observational data
-
Maathuis, M., Colombo, D., Kalisch, M., and Bühlmann, P. (2010). Predicting causal effects in large-scale systems from observational data. Nature Methods, 7:247–248.
-
(2010)
Nature Methods
, vol.7
, pp. 247-248
-
-
Maathuis, M.1
Colombo, D.2
Kalisch, M.3
Bühlmann, P.4
-
32
-
-
69949166983
-
Estimating highdimensional intervention effects from observational data
-
Maathuis, M., Kalisch, M., and Bühlmann, P. (2009). Estimating highdimensional intervention effects from observational data. Annals of Statistics, 37:3133–3164.
-
(2009)
Annals of Statistics
, vol.37
, pp. 3133-3164
-
-
Maathuis, M.1
Kalisch, M.2
Bühlmann, P.3
-
35
-
-
84937777714
-
Score-based causal learning in additive noise models
-
Nowzohour, C. and Bühlmann, P. (2015). Score-based causal learning in additive noise models. Statistics. Published online, doi:10.1080/02331888.2015.1060237.
-
(2015)
Statistics
-
-
Nowzohour, C.1
Bühlmann, P.2
-
37
-
-
84897585225
-
Identifiability of Gaussian struc tural equation models with equal error variances
-
Peters, J. and Bühlmann, P. (2014). Identifiability of Gaussian struc tural equation models with equal error variances. Biometrika, 101:219–228.
-
(2014)
Biometrika
, vol.101
, pp. 219-228
-
-
Peters, J.1
Bühlmann, P.2
-
38
-
-
84904201625
-
Causal discovery with continuous additive noise models
-
Peters, J., Mooij, J., Janzing, D., and Schölkopf, B. (2014). Causal discovery with continuous additive noise models. Journal of Machine Learning Research, 15:2009–2053.
-
(2014)
Journal of Machine Learning Research
, vol.15
, pp. 2009-2053
-
-
Peters, J.1
Mooij, J.2
Janzing, D.3
Schölkopf, B.4
-
40
-
-
84888862680
-
Estimation of regression coefficients when some of the regressors are not always observed
-
Robins, J., Rotnitzky, A., and Zhao, L. (1994). Estimation of regression coefficients when some of the regressors are not always observed. Journal of the American Statistical Association, 89:846–866.
-
(1994)
Journal of the American Statistical Association
, vol.89
, pp. 846-866
-
-
Robins, J.1
Rotnitzky, A.2
Zhao, L.3
-
41
-
-
79955139347
-
Semiparametric minimax rates
-
Robins, J., Tchetgen, E. T., Li, L., and van der Vaart, A. (2009). Semiparametric minimax rates. Electronic Journal of Statistics, 3:1305–1321.
-
(2009)
Electronic Journal of Statistics
, vol.3
, pp. 1305-1321
-
-
Robins, J.1
Tchetgen, E.T.2
Li, L.3
Van Der Vaart, A.4
-
42
-
-
77951622706
-
The central role of the propensity score in observational studies for causal effects
-
Rosenbaum, P. and Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70:41–55.
-
(1983)
Biometrika
, vol.70
, pp. 41-55
-
-
Rosenbaum, P.1
Rubin, D.2
-
44
-
-
0442278084
-
Adjusting for nonignorable drop-out using semiparametric nonresponse models (With discussion)
-
Scharfstein, D., Rotnitzky, A., and Robins, J. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models (with discussion). Journal of the American Statistical Association, 94:1096–1146.
-
(1999)
Journal of the American Statistical Association
, vol.94
, pp. 1096-1146
-
-
Scharfstein, D.1
Rotnitzky, A.2
Robins, J.3
-
45
-
-
36348990694
-
Learning graphical model structure using l1-regularization paths
-
Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press
-
Schmidt, M., Niculescu-Mizil, A., and Murphy, K. (2007). Learning graphical model structure using l1-regularization paths. In Proceedings of the National Conference on Artificial Intelligence, volume 22, page 1278. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.
-
(2007)
Proceedings of the National Conference on Artificial Intelligence
, vol.22
-
-
Schmidt, M.1
Niculescu-Mizil, A.2
Murphy, K.3
-
46
-
-
33749326177
-
-
Shimizu, S., Hoyer, P., Hyvärinen, A., and Kerminen, A. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:2003–2030.
-
(2006)
A Linear Non-Gaussian Acyclic Model for Causal Discovery. Journal of Machine Learning Research
, vol.7
, pp. 2003-2030
-
-
Shimizu, S.1
Hoyer, P.2
Hyvärinen, A.3
Kerminen, A.4
-
47
-
-
77953107844
-
Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs
-
Shojaie, A. and Michailidis, G. (2010). Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs. Biometrika, 97:519–538.
-
(2010)
Biometrika
, vol.97
, pp. 519-538
-
-
Shojaie, A.1
Michailidis, G.2
-
48
-
-
80053167144
-
An efficient algorithm for computing interventional distributions in latent variable causal models. In
-
Shpitser, I., Richardson, T. S., and Robins, J. M. (2011). An efficient algorithm for computing interventional distributions in latent variable causal models. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), pages 661–670.
-
(2011)
Proceedings of the 27Th Conference on Uncertainty in Artificial Intelligence (UAI)
, pp. 661-670
-
-
Shpitser, I.1
Richardson, T.S.2
Robins, J.M.3
-
49
-
-
0000042837
-
Evaluating functional network inference using simulations of complex biological systems
-
Smith, V. A., Jarvis, E. D., and Hartemink, A. J. (2002). Evaluating functional network inference using simulations of complex biological systems. Bioinformatics, 18(suppl 1):S216–S224.
-
(2002)
Bioinformatics
, vol.18
, pp. S216-S224
-
-
Smith, V.A.1
Jarvis, E.D.2
Hartemink, A.J.3
-
50
-
-
85032751252
-
Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models
-
Song, L., Fukumizu, K., and Gretton, A. (2013). Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models. Signal Processing Magazine, IEEE, 30:98–111.
-
(2013)
Signal Processing Magazine, IEEE
, vol.30
, pp. 98-111
-
-
Song, L.1
Fukumizu, K.2
Gretton, A.3
-
52
-
-
0003614273
-
-
MIT Press, second edition
-
Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction, and Search. MIT Press, second edition.
-
(2000)
Causation, Prediction, and Search
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
53
-
-
84868013998
-
Causal stability ranking
-
Stekhoven, D., Moraes, I., Sveinbjörnsson, G., Hennig, L., Maathuis, M., and Bühlmann, P. (2012). Causal stability ranking. Bioinformatics, 28:2819–2823.
-
(2012)
Bioinformatics
, vol.28
, pp. 2819-2823
-
-
Stekhoven, D.1
Moraes, I.2
Sveinbjörnsson, G.3
Hennig, L.4
Maathuis, M.5
Bühlmann, P.6
-
54
-
-
36348929435
-
Ordering-based search: A simple and effective algorithm for learning Bayesian networks
-
Edinburgh, Scottland, UK
-
Teyssier, M. and Koller, D. (2005). Ordering-based search: a simple and effective algorithm for learning Bayesian networks. In Proceedings of the 21th Conference on Uncertainty in Artificial Intelligence (UAI), pages 584–590, Edinburgh, Scottland, UK.
-
(2005)
Proceedings of the 21Th Conference on Uncertainty in Artificial Intelligence (UAI)
, pp. 584-590
-
-
Teyssier, M.1
Koller, D.2
-
55
-
-
84902481901
-
On the uniform convergence of empirical norms and inner products, with application to causal inference
-
van de Geer, S. (2014). On the uniform convergence of empirical norms and inner products, with application to causal inference. Electronic Journal of Statistics, 8:543–574.
-
(2014)
Electronic Journal of Statistics
, vol.8
, pp. 543-574
-
-
Van De Geer, S.1
-
56
-
-
84879140981
-
ℓ0-penalized maximum likelihood for sparse directed acyclic graphs
-
0-penalized maximum likelihood for sparse directed acyclic graphs. Annals of Statistics, 41:536–567.
-
(2013)
Annals of Statistics
, vol.41
, pp. 536-567
-
-
Van De Geer, S.1
Bühlmann, P.2
-
59
-
-
22044448669
-
Sparse graphical gaussian modeling of the isoprenoid gene network in arabidopsis thaliana
-
Wille, A., Zimmermann, P., Vranová, E., Fürholz, A., Laule, O., Bleuler, S., Hennig, L., Prelic, A., von Rohr, P., Thiele, L., et al. (2004). Sparse graphical gaussian modeling of the isoprenoid gene network in arabidopsis thaliana. Genome Biol, 5(11):R92.
-
(2004)
Genome Biol
, vol.5
, Issue.11
-
-
Wille, A.1
Zimmermann, P.2
Vranová, E.3
Fürholz, A.4
Laule, O.5
Bleuler, S.6
Hennig, L.7
Prelic, A.8
Von Rohr, P.9
Thiele, L.10
-
62
-
-
12344259602
-
Advances to bayesian network inference for generating causal networks from observational biological data
-
Yu, J., Smith, V. A., Wang, P. P., Hartemink, A. J., and Jarvis, E. D. (2004). Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics, 20:3594–3603.
-
(2004)
Bioinformatics
, vol.20
, pp. 3594-3603
-
-
Yu, J.1
Smith, V.A.2
Wang, P.P.3
Hartemink, A.J.4
Jarvis, E.D.5
-
63
-
-
52949097616
-
On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
-
Zhang, J. (2008). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172:1873–1896.
-
(2008)
Artificial Intelligence
, vol.172
, pp. 1873-1896
-
-
Zhang, J.1
|