Do Finetti: On Causal Effects for Exchangeable Data

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.

Authors

• Siyuan Guo
• Chi Zhang
• Karthika Mohan
• Ferenc Huszár
• Bernhard Schölkopf

Keywords

No keywords are indexed for this paper.