Estimating causal effects of treatments in randomized and nonrandomized studies.
01 Oct 1974-Journal of Educational Psychology (American Psychological Association)-Vol. 66, Iss: 5, pp 688-701
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.
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 which
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01 Jan 2021
TL;DR: In this paper, the authors investigate the influence of the role of a fonds de capital-investissement on the performance of a PME re-repartition of an entreprise.
Abstract: Cet article etudie l’incidence des modalites de controle et le role des fonds de capital-investissement sur la croissance et la performance des PME francaises reprises par leurs dirigeants. Selon la theorie de l’agence, le rachat d’une entreprise par ses cadres dirigeants (REC) offre de nombreux avantages : nouvelles capacites de financement, reduction des couts d’agence, amelioration de la performance operationnelle. Ces effets positifs doivent cependant etre nuances au regard de la reduction des investissements et de l’augmentation des couts de faillite. L’etude empirique est construite sur un modele d’appariement par score de propension a partir d’un echantillon de 208 operations de REC realisees sur des PME francaises entre 2002 et 2012. Les resultats montrent que la croissance des PME reprises n’est pas plus elevee que celle des firmes comparables n’ayant fait l’objet d’un REC. Par ailleurs, si la croissance des PME reprises semble s’accelerer apres le rachat, l’influence de la presence d’un fonds de capital-investissement dans ce processus n’apparait pas significative.
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TL;DR: In this article, the authors considered a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment, and they used control variables to give necessary and sufficient conditions for identification of average treatment effects.
Abstract: Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the generalized propensity scores (Imbens, 2000) are bounded away from zero with probability one, a simple identification condition is that their sum be bounded away from one with probability one. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
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TL;DR: In this article, a German targeted wage support program aimed at providing incentives to accept lower paid wage offers was proposed to address the problem of high reservation wages among older unemployed individuals, and sent out information brochures on this program to randomly selected eligible men.
Abstract: To address the problem of high reservation wages among older unemployed individuals, a German targeted wage support program aimed at providing incentives to accept lower paid wage offers. We sent out information brochures on this program to randomly selected eligible men. The treatment significantly increased awareness of the program by 20 percentage points. Combining survey and administrative data, we conduct reduced form estimates of the effects of brochure receipt on recipients and estimate local average treatment effects of additional program knowledge. The information treatment significantly increased take-up rates of the program. For unemployed men aged 50--54, we find no positive effects on employment outcomes, thus the additional take-up seems to have been pure windfall. For unemployed men aged 55--59, however, we find some positive effects of additional information on labor market results. The labor market status of unemployed men above age 60 is not affected by brochure receipt.
Cites methods from "Estimating causal effects of treatm..."
...Non-experimental evaluations partly apply duration models (Abbring/van den Berg 2003) while many others rely on statistical matching methods to choose an adequate comparison group (Rubin 1974)....
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TL;DR: The causal optimal transport (COT) as mentioned in this paper is a common tool to de-bias estimates of causal effects, which directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or more generally between a source and target population.
Abstract: Weighting methods are a common tool to de-bias estimates of causal effects. And though there are an increasing number of seemingly disparate methods, many of them can be folded into one unifying regime: causal optimal transport. This new method directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or, more generally, between a source and target population. Our approach is model-free but can also incorporate moments or any other important functions of covariates that the researcher desires to balance. We find that the causal optimal transport outperforms competitor methods when both the propensity score and outcome models are misspecified, indicating it is a robust alternative to common weighting methods. Finally, we demonstrate the utility of our method in an external control study examining the effect of misoprostol versus oxytocin for treatment of post-partum hemorrhage.
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Book•
01 Jan 1925
TL;DR: The prime object of as discussed by the authors is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
Abstract: The prime object of this book is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
11,308 citations
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01 Jan 1959
TL;DR: The general decision problem, the Probability Background, Uniformly Most Powerful Tests, Unbiasedness, Theory and First Applications, and UNbiasedness: Applications to Normal Distributions, Invariance, Linear Hypotheses as discussed by the authors.
Abstract: The General Decision Problem.- The Probability Background.- Uniformly Most Powerful Tests.- Unbiasedness: Theory and First Applications.- Unbiasedness: Applications to Normal Distributions.- Invariance.- Linear Hypotheses.- The Minimax Principle.- Multiple Testing and Simultaneous Inference.- Conditional Inference.- Basic Large Sample Theory.- Quadratic Mean Differentiable Families.- Large Sample Optimality.- Testing Goodness of Fit.- General Large Sample Methods.
6,480 citations
TL;DR: In this paper, the basic theory of analysis of variance by considering several different mathematical models is examined, including fixed-effects models with independent observations of equal variance and other models with different observations of variance.
Abstract: Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.
5,728 citations