Journal ArticleDOI
The practical importance of understanding placebo effects and their role when approving drugs and recommending doses for medical practice
Donald B. Rubin,Donald B. Rubin +1 more
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The general reliance on blinded placebo-controlled randomized trials, both to approve drugs and to set their recommended dosages, although statistically sound for some purposes, may be statistically naïve in the context of guiding general medical practice.Abstract:
The general reliance on blinded placebo-controlled randomized trials, both to approve drugs and to set their recommended dosages, although statistically sound for some purposes, may be statistically naive in the context of guiding general medical practice. Briefly, the reason is that medical prescriptions are unblinded, and so patients who receive drugs in practice receive both the active medical effect of the drug, as estimated in blinded trials, as well as any “placebo effects”, rarely carefully defined or estimated, but intuitively defined as the extra effect, on “you”, when you think you are being actively treated, even when in fact you may not be receiving anything that actually works.read more
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Essential concepts of causal inference: a remarkable history and an intriguing future
TL;DR: Much of what is written about causal inference is found to be mathematically inapposite in one of these senses because the descriptions either include irrelevant clutter or omit conditions required for the correctness of the assertions.
References
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TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
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Judgement under Uncertainty: Heuristics and Biases
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Statistics and Causal Inference
TL;DR: In this article, the authors use a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference.
Journal Article
Identification of Causal effects Using Instrumental Variables
TL;DR: In this paper, a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable.