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instrumental_variables
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matteocourthoud committed Nov 12, 2023
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"In many set"
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"## Evaluating Subscriptions"
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"In the rest of the article, we are going to use a toy example. Suppose you were an online company and you have a weekly **newsletter**. You would like to understand whether the newsletter is worth the effort and it is actually successfull in increasing sales. Unfortunately, you cannot run a standard A/B test since you cannot force customers to subscribe to the newsletter. Does it mean you cannot evaluate the newsletter? Not exactly. \n",
"\n"
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"## Instrumental Variables and Machine Learning"
"## The Limits of IV"
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"Before concluding, I would like to point towards an interesting feature of instrumental variable"
"The main limit of instrumental variables in an expeerimental setting, as analyzed in this article, is that they estimate a very \"special\" causal effect. As we have seen in the previous section, we have to re-scale the total effect by the number of customers decided to `subscribe` because of the `newsletter`. This means that we can estimate the effect only for those customer that we *complied* with our interventions. This set of customers is usually called **compliers** and the corresponding causal effect is someimes called the **Local Average Treatment Effect (LATE)** or **Compliers Average Causal Effect (CACE)**.\n",
"\n",
"We are unfortunately unable to say anything for what concerns customers that subscribed to the `newsletter` even without the `notification`, called the **always-takers**, and those that we couldn't convince with the `notification`, the **never-takers**."
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