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Compute ITT, TOT, and LATE with noncompliance

NetflixData Scientist · Onsite
MediumData ScientistOnsiteAnalytics & Experimentation

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This question evaluates understanding of causal inference and experimental analysis, specifically estimation and interpretation of ITT, TOT, and LATE under noncompliance along with classification of compliance types (compliers, always-takers, never-takers, defiers).

In the same personalization experiment, not everyone assigned to treatment actually receives personalization (noncompliance).

You are given user-level columns:

  • Z (0/1): randomized assignment (instrument)
  • D (0/1): actually received personalization (treatment received)
  • Y (float): minutes_streamed

Tasks:

  1. Define and compute the Intention-to-Treat (ITT) effect of assignment on minutes streamed.
  2. Define what people often mean by Treatment-on-the-Treated (TOT) in this setting, and compute it.
  3. Using an IV interpretation, compute the LATE for compliers and explain what assumptions are required.
  4. Conceptually classify users into compliance types (compliers, always-takers, never-takers, defiers) and state which group LATE pertains to.