Split-door criterion: Identification of causal effects through auxiliary outcomes

28 Nov 2016Amit SharmaJake M. HofmanDuncan J. Watts

We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available. Specifically, we examine what we call the split-door setting, where the outcome variable can be split into two parts: one that is potentially affected by the cause being studied and another that is independent of it, with both parts sharing the same (unobserved) confounders... (read more)

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