The covariance of causal effect estimators for binary v-structures
By: Jack Kuipers, Giusi Moffa
Potential Business Impact:
Combines two ways to find causes for better results.
Previously [Journal of Causal Inference, 10, 90-105 (2022)], we computed the variance of two estimators of causal effects for a v-structure of binary variables. Here we show that a linear combination of these estimators has lower variance than either. Furthermore, we show that this holds also when the treatment variable is block randomised with a predefined number receiving treatment, with analogous results to when it is sampled randomly.
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