Journal ArticleDOI
Estimating causal effects of treatments in randomized and nonrandomized studies.
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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.Abstract:
A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating causal effects of treatments. The basic conclusion is that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases. Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The implication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects. If taken as applying to all fields of study, this position is untenable. Since the extensive use of randomized experiments is limited to the last half century,8 and in fact is not used in much scientific investigation today,4 one is led to the conclusion that most scientific "truths" have been established without using randomized experiments. In addition, most of us successfully determine the causal effects of many of our everyday actions, even interpersonal behaviors, without the benefit of randomization. Even if the position that causal effects of treatments can only be well established from randomized experiments is taken as applying only to the social sciences in whichread more
Citations
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Book
Econometric Analysis of Cross Section and Panel Data
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Journal ArticleDOI
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
BookDOI
Designing Social Inquiry: Scientific Inference in Qualitative Research
TL;DR: For instance, King, Keohane, Verba, and Verba as mentioned in this paper have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable.
Journal ArticleDOI
Some practical guidance for the implementation of propensity score matching
Marco Caliendo,Sabine Kopeinig +1 more
TL;DR: Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects as discussed by the authors, but empirical examples can be found in very diverse fields of study, and each implementation step involves a lot of decisions and different approaches can be thought of.
References
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Book
The design and analysis of experiments.
TL;DR: In this article, Monterey describes a books design and analysis of experiments, and the pronouncement as without difficulty as perspicacity of this design and analyses of experiments montgomery can be taken as skillfully as picked to act.
Book
The design and analysis of industrial experiments
TL;DR: This paper is based on a lecture on the “Design and Analysis of Industrial Experiments” given by Dr O. L. Davies on the 8th of May 1954 and the recent designs developed by Box for the exploration of response surfaces are briefly considered.
Book ChapterDOI
Controlling Bias in Observational Studies: A Review.
TL;DR: This article reviewed the effectiveness of matched sampling and statistical adjustment, alone and in combination, in reducing bias due to confounding x-variables when comparing two populations, and the adjustment methods were linear regression adjustment for x continuous and direct standardization for x categorical.
Journal ArticleDOI
The Design and Analysis of Experiments
TL;DR: Oscar Kempthorne as discussed by the authors, The Design and Analysis of Experiments. New York: John Wiley and Sons; London: Chapman and Hall, 1952. Pp. xix + 631.