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Casual Inferences
Real organizations are incredibly messy and real processes often don’t respect the assumptions required by A/B testing:
- some teams forgot to keep aside a control group;
- some teams kept aside a control group which is not similar enough to the treated group;
- some teams kept aside a control group that is too small;
- different teams sent conflicting marketing campaigns (e.g. retention campaigns and up-selling campaigns) to the same users.
- some teams sent a marketing campaign to users that belong to the control group of another team.
Causal ML allows simulating different universes by allowing us to answer counterfactual questions.
Counterfactual question: “We did this action. Afterward, the average user spending was 100 $. But how do we know what they would have spent if we didn’t do our action?”
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💡 How casual inference helps?
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For example, let’s say we want to simulate two scenarios:
- Universe A: What would have happened if we didn’t send the campaign to any user?
- Universe B: What would have happened if we sent the campaign to all the users?