Why SUTVA is crucial for Causal inference | Importance of SUTVA in A/B testing | Violation Examples
Channel: DataTrek
Date: 2023-03-27

#SUTVA #causalinference #randomizedexperiments #treatmenteffect #potentialoutcomes #interference #consistency #ignorability #selectionbias #spillovereffect #statisticalanalysis #researchmethods #dataanalysis #datainsights #datascience #machinelearning #statistics #experimentaldesign #researchstudy #scientificmethod #datainterpretation #researchfindings #empiricalresearch #ML #AI #machinelearning 

SUTVA stands for Stable Unit Treatment Value Assumption. It is a fundamental assumption of causal inference in statistics, which states that the potential outcomes of a unit should only depend on the treatment that it receives, and not on the treatment assigned to other units.


In other words, SUTVA assumes that there is no spillover effect or interference between the units. This assumption is necessary for valid estimation of causal effects in randomized experiments.


Necessary properties to satisfy SUTVA are following.
No interference: The treatment given to one unit should not affect the potential outcomes of other units. Each unit's treatment should only affect that unit and not others.
Consistency: SUTVA assumes that the potential outcomes or the  treatment effect is the same for all units.
Ignorability: SUTVA assumes that the assignment of treatment to a unit is independent of the potential outcomes of that unit, given the observed covariates. In other words, there should be no selection bias in the assignment of the treatment.


Some examples where SUTVA is violated
1. Imagine if testing the effect of a new medication, If some patients in the control group are roommates with patients in the treatment group and are exposed to the medication through inhalation or absorption, then SUTVA is violated.
2. If people in treatment or control group are talking with each other & hence there is an interference in the response, SUTVA is violated.
3. Another interesting example is from Facebook's Supplier side A/B paper. Suppose Facebook wants to help a certain type of sellers on their marketplace by giving them better visibility in search results. They run an experiment where they randomly assign 1% of these sellers to a "treatment" group that gets this extra visibility, and another 1% to a "control" group that doesn't. During the experiment, the treatment group sees a significant increase in sales and engagement compared to the control group, suggesting that the initiative is working. But when Facebook tries to scale up the initiative to include more sellers, they find that the treatment effect is not consistent - the response varies depending on how many other sellers are also getting the treatment due to the increased competition for the limited slots. This violates SUTVA because the effect on each seller depends not just on whether they got the boost or not, but also on whether other sellers got the boost as well.

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