UAI 2021poster4 citations
Condition number bounds for causal inference
Spencer L. Gordon, Vinayak M. Kumar, Leonard J. Schulman, Piyush Srivastava
Abstract
An important achievement in the field of causal inference was a complete characterization of when a causal effect, in a system modeled by a causal graph, can be determined uniquely from purely observational data. The identification algorithms resulting from this work produce exact
BibTeX
@InProceedings{pmlr-v161-gordon21a,
title = {Condition number bounds for causal inference},
author = {Gordon, Spencer L. and Kumar, Vinayak M. and Schulman, Leonard J. and Srivastava, Piyush},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {1948--1957},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/gordon21a/gordon21a.pdf},
url = {https://proceedings.mlr.press/v161/gordon21a.html},
abstract = {An important achievement in the field of causal inference was a complete characterization of when a causal effect, in a system modeled by a causal graph, can be determined uniquely from purely observational data. The identification algorithms resulting from this work produce exact