My understanding is that the `difference between 0 + epsilon and 0` is you’d give different treatments to a particular person. Suppose you had a treatment and a control and found the individual treatment effect for a particular person is + epsilon. Then you should give the person the treatment. I can see this making a difference if you want to get an ‘accuracy correct treatment’ stat or something like that but seems irrelevant for really small ITEs.

]]>The abstract says that type-S error is “the probability of the model inferring the sign of the treatment effect wrong.” Is this something that only makes sense in a causal context? What does it mean to infer the sign of a treatment effect? Is it something like bounding the posterior away from zero like in a NHST? What’s the difference between 0 + epsilon and 0 – epsilon as an estimate in a regression? Don’t they both have roughly no effect?

I tried to read the paper, but got lost in the examples, definitions, and theorems.

]]>