Compound Treatments and Transportability of Causal Inference

By Miguel A. Hern'an, Tyler J. VanderWeele. 2011

293 citations recorded by the source corpus.

Ill-defined causal questions present serious problems for observational studies—problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, “prevention of obesity”). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of “transportability” are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.

Subfields: Statistics and Probability

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Citations in the corpus

Social Networks and Causal Inference (2013, background): “Moreover, different possible interventions (e.g., exercise, diet, or liposuction) may have different effects in terms of influencing other individuals within the network (cf. Hernán and VanderWeele 2011).”
Causal Inference Under Multiple Versions of Treatment (2013, unknown): “In clinical trials, guidelines are often given to reduce the number or relevance of versions but it is generally not possible to eliminate this problem entirely [10].”
Generalizing causal inferences from randomized trials: counterfactual and graphical identification (2019, background): “A finer representation of trial engagement, however, does not provide additional conceptual insights so we chose to build our discussion around the compound treatments R and S [26].”
A General Algorithm for Deciding Transportability of Experimental Results (2013, background): “Hernán and VanderWeele [52] addressed a problem related to transportability in the context of “ compound treatments, ” namely, treatments that can be implemented in multiple versions (e.g., “ exercise at least 15 minutes a day ” ).”
The Statistics of Causal Inference: A View from Political Methodology (2015, unknown): “Herna´n and VanderWeele (2011) argue that the consistency assumption must be evaluated by analysts since it links observed data to the counterfactual outcomes.”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “…and common biases (41), facilitates a systematic methodologic evaluation of observational studies (42, 43) and the transportability of their estimates (44 – 46), and may help explain between-studies differences (47) as different observational analyses may be emulating different target trials.”