P15: Bias of Causal Identification using Non-IID Data

Authors: Chi Zhang, Karthika Mohan, Judea Pearl

Abstract: Traditional causal inference techniques assume data are independent and identically distributed (IID) and thus ignores interactions among units. In this paper, we analyze the bias of causal identification in linear models if IIDness is falsely assumed. Specifically, we discuss 1) when it is safe to apply traditional IID methods on non-IID data, 2) how large the bias is if IID methods are blindly applied, and 3) how to correct the bias. We present the results through a real-world example about estimating the causal effect of vaccination on sickness.