Observational-interventional priors for dose-response learning

Advances in Neural Information Processing Systems

Volumn , Issue , 2016, Pages 1569-1577

  Silva, Ricardo ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AFFINE TRANSFORMS; SENSITIVITY ANALYSIS;

COGNITIVE SKILL; DOSE-RESPONSE CURVES; GAUSSIAN PROCESS PRIORS; INTERVENTIONAL; NON-PARAMETRIC; OBSERVATIONAL DATA; PREMATURE INFANTS; TREATMENT LEVEL;

SELF ORGANIZING MAPS;

EID: 85018923275     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. E. Bareinboim and J. Pearl. Causal inference from Big Data: Theoretical foundations and the data-fusion problem. Proceedings of the National Academy of Sciences, in press, 2016.
2. M. J. Bayarri, J. O. Berger, R. Paulo, J. Sacks, J. A. Cafeo, J. Cavendish, C.-H. Lin, and J. Tu. A framework for validation of computer models. Technometrics, 49:138-154, 2007.
3. J. Brooks-Gunn, F. Liaw, and P. Klebanov. Effects of early intervention on cognitive function of low birth weight preterm infants. Journal of Pediatrics, 120:350-359, 1991.
4. A. Damianou and N. D. Lawrence. Deep Gaussian processes. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS), pages 207-215, 2013.
5. J. Ernest and P. Bühlmann. Marginal integration for nonparametric causal inference. Electronic Journal of Statistics, 9:3155-3194, 2015.
6. R. Gramacy and H. K. Lee. Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103:1119-1130, 2008.
7. J. Hill. Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20:217-240, 2011.
8. J. Hill and Y.-S. Su. Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes. The Annals of Applied Statistics, 7:1386-1420, 2013.
9. A. Hyttinen, F. Eberhardt, and P. O. Hoyer. Experiment selection for causal discovery. Journal of Machine Learning Research, 14:3041-3071, 2013.
10. F. Liu, M. J. Bayarri, and J. O. Berger. Modularization in Bayesian analysis, with emphasis on analysis of computer models. Bayesian Analysis, 4:119-150, 2009.
11. D. J. C. MacKay. Information-based objective functions for active data selection. Neural Computation, 4:590-604, 1992.
12. D. J. C. MacKay. Bayesian non-linear modelling for the prediction competition. ASHRAE Transactions, 100:1053-1062, 1994.
13. L. C. McCandless, P. Gustafson, and A. R. Levy. Bayesian sensitivity analysis for unmeasured confounding in observational studies. Statistics in Medicine, 26:2331-2347, 2007.
14. S. L. Morgan and C. Winship. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press, 2014.
15. R. Neal. Slice sampling. The Annals of Statistics, 31:705-767, 2003.
16. J. Pearl. Causality: Models, Reasoning and Inference. Cambridge University Press, 2000.
17. C. Rasmussen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
18. J. Robins, M. Sued, Q. Lei-Gomez, and A. Rotnitzky. Comment: Performance of doublerobust estimators when "inverse probability" weights are highly variable. Statistical Science, 22:544-559, 2007.
19. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. Cambridge University Press, 2000.
20. T. VanderWeele and I. Shpitser. A new criterion for confounder selection. Biometrics, 64:1406-1413, 2011.