A General Identification Condition for Causal Effects
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 also called 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.
Cite
Text
Tian and Pearl. "A General Identification Condition for Causal Effects." AAAI Conference on Artificial Intelligence, 2002. doi:10.5555/777092.777180Markdown
[Tian and Pearl. "A General Identification Condition for Causal Effects." AAAI Conference on Artificial Intelligence, 2002.](https://mlanthology.org/aaai/2002/tian2002aaai-general/) doi:10.5555/777092.777180BibTeX
@inproceedings{tian2002aaai-general,
title = {{A General Identification Condition for Causal Effects}},
author = {Tian, Jin and Pearl, Judea},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2002},
pages = {567-573},
doi = {10.5555/777092.777180},
url = {https://mlanthology.org/aaai/2002/tian2002aaai-general/}
}