Do Finetti: On Causal Effects for Exchangeable Data
CoRR(2024)
摘要
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.
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