Effects of treatment on the treated: Identification and generalization

Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

Volumn , Issue , 2009, Pages 514-521

  Shpitser, Ilya ^a   Pearl, Judea ^b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CAUSAL ANALYSIS; CAUSAL DIAGRAM; EDUCATIONAL PROGRAM; INDIVIDUAL DECISION MAKING; INTERVENTIONAL; OBSERVATIONAL STUDY;

DECISION MAKING;

EID: 80053137537     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. J. J. Heckman. Randomization and social policy evaluation. In: Evaluations: Welfare and Training Programs, Cambridge, MA: Harvard University Press, 1992.
2. J. Neyman. Sur les applications de la thar des probabilities aux experiences Agaricales: Essay des principle. Excerpts reprinted (1990) in English. Statistical Science, 5:463-472, 1923.
3. Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan and Kaufmann, San Mateo, 1988.
4. Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
5. Thomas Richardson and Peter Spirtes. Ancestral graph markov models. Annals of Statistics, 30:962-1030, 2002. (Pubitemid 37095336)
6. James M. Robins, VanderWeele Tyler J., and Thomas S. Richardson. Comment on causal effects in the presence of non compliance: a latent variable interpretation by antonio forcina. METRON, LXIV(3):288-298, 2006.
7. D. B. Rubin. Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66:688-701, 1974.
8. Ilya Shpitser and Judea Pearl. Identification of conditional interventional distributions. In Uncertainty in Artificial Intelligence, volume 22, 2006.
9. Ilya Shpitser and Judea Pearl. Identification of joint interventional distributions in recursive semimarkovian causal models. In 21st National Conference on Artificial Intelligence, 2006.
10. Ilya Shpitser and Judea Pearl. What counterfactuals can be tested. In 23rd Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2007.
11. Ilya Shpitser and Judea Pearl. Effects of treatment on the treated: Identification and generalization. Technical Report R-349-L, Cognitive Systems Laboratory, University of California, Los Angeles, 2009.
12. Jin Tian. Studies in Causal Reasoning and Learning. PhD thesis, Department of Computer Science, University of California, Los Angeles, 2002.
13. Jin Tian and Judea Pearl. A general identification condition for causal effects. In 18th National Conference on Artificial Intelligence, pages 567-573, 2002.
14. T. S. Verma and Judea Pearl. Equivalence and synthesis of causal models. Technical Report R-150, Department of Computer Science, University of California, Los Angeles, 1990.