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Bias-Variance TradeoffsΒΆ

IS-based estimators are unbiased when propensities are correct and overlap holds, but they can have high variance. WIS/PDIS can be biased in finite samples. Model-based estimators reduce variance but introduce model bias. DR methods aim to balance both by combining model estimates with importance weights.

Key takeaways:

  • Long horizons magnify variance in IS/PDIS.
  • WDR and MAGIC can stabilize estimates when models are reasonable.
  • FQE is attractive when propensities are unknown but model fit is reliable.