ABCD-strategy: Budgeted experimental design for targeted causal structure discovery
© 2019 by the author(s). Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited interventional data. Given that randomized experiments are usually expensive to perform, we propo...
Main Authors: | Uhler, Caroline, Agrawal, Raj, Squires, Chandler, Yang, Karren, Shanmugam, Karthikeyan |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | English |
Published: |
2021
|
Online Access: | https://hdl.handle.net/1721.1/137070 |
Similar Items
-
Causal Structure Learning: A Combinatorial Perspective
by: Squires, Chandler, et al.
Published: (2022) -
Causal structure discovery from incomplete data
by: Squires, Chandler(Chandler B.)
Published: (2020) -
Size of interventional Markov equivalence classes in random DAG models
by: Katz, Dmitriy, et al.
Published: (2022) -
DCI: learning causal differences between gene regulatory networks
by: Belyaeva, Anastasiya, et al.
Published: (2022) -
Direct Estimation of Differences in Causal Graphs
by: Wang, Yuhao, et al.
Published: (2021)