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Causal Inference and Experiments

Standardise the schedule comparison

BOTIE

Subtract control stratum means from treatment stratum means, then take their weighted average using target proportions.
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Context for this challenge

Subtract control stratum means from treatment stratum means, then take their weighted average using target proportions.

Botie needs both schedules compared in the same target population rather than in different mixtures of starting conditions.

Subtract control from treatment within each stratum, validate the target proportions, then calculate their weighted average.

Examples and results
Treatment stratum means
  1. 04
  2. 19
Control stratum means
  1. 03
  2. 18
Target stratum proportions
  1. 00.6
  2. 10.4
Required result
1
Treatment stratum means
  1. 04
  2. 18
Control stratum means
  1. 03
  2. 14
Target stratum proportions
  1. 00.8
  2. 10.2
Required result
1.6
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Assumptions and test precision

Causal interpretation requires a sufficient adjustment set, consistency, overlap, and the other stated design assumptions. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.

Compare both groups in the same target population

This check uses the pieces from “Weight condition-specific contrasts by one target mixture”.

BOTIE

Botie has treated and control recovery rates for mild and severe cases. The target population is 60% mild and 40% severe. Calculate the target-weighted within-group difference, keeping both treatments on that same population mix.
Treatment stratum means
  1. 00.9
  2. 10.5
Control stratum means
  1. 00.8
  2. 10.4
Target stratum proportions
  1. 00.6
  2. 10.4

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Which assumptions are needed before this standardised contrast can describe a causal effect?

A common target mixture solves a weighting problem; sufficient adjustment, overlap, and consistency still require justification.

Causal interpretation requires a sufficient adjustment set, consistency, overlap, and the other stated design assumptions.

Work through the Python challenges →
Continue the courseReweighting Records Without Inventing Missing Evidence →
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