A General Identification Condition for Causal Effects (2002)
by
Jin Tian
,
Judea Pearl
| Citations: | 47 - 17 self |
BibTeX
@MISC{Tian02ageneral,
author = {Jin Tian and Judea Pearl},
title = {A General Identification Condition for Causal Effects},
year = {2002}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called "causal graph", in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion for the effects of a singleton variable on any set of variables.
Citations
| 5664 | Probabilistic Reasoning in Intelligent Systems - Pearl - 1988 |
| 766 | Causality: Models, reasoning, and inference - Pearl - 2001 |
| 384 | Causation, prediction and search - Spirtes, Glymour, et al. - 1993 |
| 79 | Comment: Graphical models, causality, and intervention - Pearl - 1993 |
| 78 | Causal diagrams for epidemiologic research. Epidemiology - Greenland, Pearl, et al. |
| 45 | A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. J Chron Dis 40(Suppl 2):139S-161S - Robins - 1987 |
| 34 | Causal diagrams for experimental research. Biometrika 82(4):669–710 - Pearl - 1995 |
| 32 | Testing identifiability of causal effects - Galles, Pearl - 1995 |
| 14 | Graphical models for causal inference - Lauritzen - 2000 |
