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Related Experiment Video

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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Published on: September 27, 2019

Identification of causal effects on binary outcomes using structural mean models.

Paul S Clarke¹, Frank Windmeijer

¹Centre for Market & Public Organisation, University of Bristol, 1TX, UK. paul.clarke@bristol.ac.uk

Biostatistics (Oxford, England)

|June 5, 2010

Summary

This summary is machine-generated.

Structural mean models (SMMs) estimate causal effects in trials with noncompliance. Additive and multiplicative SMMs identify local causal effects under monotonicity, but interpretation requires careful consideration of underlying assumptions.

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Area of Science:

Biostatistics
Epidemiology
Causal Inference

Background:

Structural Mean Models (SMMs) were developed for causal effect estimation in randomized trials with nonignorable treatment noncompliance.
Existing literature confirms SMMs can identify causal effects in placebo-controlled trials under weak assumptions.

Purpose of the Study:

To evaluate the application of SMMs in broader study types beyond randomized controlled trials.
To critically examine the 'no effect modification' assumption crucial for SMM identification in new contexts.
To clarify the interpretation of SMM inferences, particularly regarding different SMM estimators.

Main Methods:

The study analyzes the assumptions underlying SMM identification in non-randomized or complex settings.
It investigates the impact of treatment selection assumptions, specifically monotonicity.
An application and simulation study are used to demonstrate and verify findings.

Main Results:

The 'no effect modification' assumption for SMMs is shown to be highly dependent on the unknown data-generating causal model, making it difficult to justify.
Additive and multiplicative SMMs are confirmed to identify local (complier) causal effects when treatment selection is monotonic.
The double-logistic SMM estimator does not identify local causal effects without additional assumptions.

Conclusions:

The justification of SMM assumptions requires careful consideration of the specific causal model.
While certain SMM variants can estimate complier causal effects under monotonicity, their interpretation is nuanced.
The study provides clarity on the proper interpretation of SMM inferences through practical examples and simulations.