@inproceedings{
guo2024do,
title={Do Finetti: On Causal Effects for Exchangeable Data},
author={Siyuan Guo and Chi Zhang and Karthika Mohan and Ferenc Husz{\'a}r and Bernhard Sch{\"o}lkopf},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4rCZeCZAON}
}
We study causal effect estimation in a setting where the data are not i.i.d.$\ $(independent and identically distributed). We focus on exchangeable data satisfying an assumption of independent causal mechanisms. Traditional causal effect estimation frameworks, e.g., relying on structural causal models and do-calculus, are typically limited to i.i.d. data and do not extend to more general exchangeable generative processes, which naturally arise in multi-environment data. To address this gap, we develop a generalized framework for exchangeable data and introduce a truncated factorization formula that facilitates both the identification and estimation of causal effects in our setting. To illustrate potential applications, we introduce a causal Pólya urn model and demonstrate how intervention propagates effects in exchangeable data settings. Finally, we develop an algorithm that performs simultaneous causal discovery and effect estimation given multi-environment data.
Do Finetti: On Causal Effects for Exchangeable Data
Siyuan Guo Chi Zhang Karthika Mohan Ferenc Huszár Bernhard Schölkopf