Intention to treat (ITT) analyses code a participant’s group according to the condition they were assigned to, regardless of whether they engaged with that condition or, e.g., swapped to another. Sometimes the average treatment effect on the treated (ATT) estimand is misunderstood to mean analysing people according to what intervention they actually engaged with. But the two concepts are independent of each other and depend on how the potential outcomes are defined.
For example, let Yᵢ(ai) denote the potential outcome for participant, i, were they to be assigned to the intervention, and Yᵢ(ac) denote the potential outcome were they to be assigned to some comparison condition. Let Gᵢ denote the group they were assigned to (note, not necessarily what they engaged with!). The following estimand is an ITT ATT:
E[Yᵢ(ai) – Yᵢ(ac) | Gᵢ = ai]
Tag: potential outcomes
Your control group can be sensible
There’s nothing in theories of experiments requiring that researchers use a ridiculous control group. An example of such a ridiculous (and unethical) control would include randomising people who are living in abject poverty either to a regular sufficient social security payment or leaving them in poverty. Hmmm, what group will have a better outcome…?
The first example Rubin gave in his classic “Estimating causal effects of treatments in randomized and nonrandomized studies” (Rubin, 1974, p. 689) had the following two groups:
“… we will restrict discussion to the very simple study consisting of 2N units (e.g., subjects), half having been exposed to an experimental (E) treatment (e.g., a compensatory reading program) and the other half having been exposed to a control (C) treatment (e.g., a regular reading program).”
Lind’s (1753) study of scurvy treatments compared six treatments, including cider, vinegar, and sea water, as well as the oranges and lemons. Potentially unclear what the theoretical rationale was for all of those, so not ideal I’ll admit.
It’s pretty common now to use “business as usual”, “treatment as usual”, “teaching as usual”, etc., controls and to avoid waitlist controls. But this is not a new idea.
Additionally, Rubin didn’t use Y(1) and Y(0) for the two potential outcomes, but Y(E) and Y(C). It’s a very subtle difference; however, I wonder if ditching the zero helps people’s understanding. My preference would be something even closer to the names of the actual programs, e.g., if we are evaluating whether the Acme Compensatory Reading Programme is better than regular reading, Y(Acme) and Y(Regular) to emphasise the contrast. (Also now we would use “participants” rather than “subjects”.)
References
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.
Lind, J. (1753). A treatise of the scurvy. In three parts. Containing an inquiry into the nature, causes and cure, of that disease. Together with a critical and chronological view of what has been published on the subject. Edinburgh: Printed by Sands, Murray and Cochran for A. Kincaid and A. Donaldson.