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## Contributions: Abstract## Testing causal effects
We study the effect on a outcome or response variable of a treatment 0 relative to another treatment 1 for a population of Causal inference is based on missing values. Special assumptions for the assignment mechanism, which describes the assigning of treatments to units, allow to construct tests. Rubin (1990) presents an approach for testing the null hypothesis of zero individual causal effect for all units against the alternative of positive individual causal effect. Under the null hypothesis it is possible to determine a A modification of the assumptions makes it possible to use Rubin's model for a null hypothesis of zero average causal effect without the condition of constant additive individual causal effects. To calculate |