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SUTVA
The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis,Neyman, Jerzy. ''Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes.'' Master's Thesis (1923). Excerpts reprinted in English, Statistical Science, Vol. 5, pp. 463–472. ( D. M. Dabrowska, and T. P. Speed, Translators.) though he discussed it only in the context of completely randomized experiments. Rubin extended it into a general framework for thinking about causation in both observational and experimental studies. Introduction The Rubin causal model is based on the idea of potential outcomes. For example, a person would have a particular income at age 40 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Average Treatment Effect
The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials. The ATE measures the difference in mean (average) outcomes between units assigned to the treatment and units assigned to the control. In a randomized trial (i.e., an experimental study), the average treatment effect can be estimated from a sample using a comparison in mean outcomes for treated and untreated units. However, the ATE is generally understood as a causal parameter (i.e., an estimate or property of a population) that a researcher desires to know, defined without reference to the study design or estimation procedure. Both observational studies and experimental study designs with random assignment may enable one to estimate an ATE in a variety of ways. General definition Originating from early statistical analysis in the fields of agriculture and medicine, the term "treatment" is now applied, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Analysis
Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability.Upton, G., Cook, I. (2008) ''Oxford Dictionary of Statistics'', OUP. . Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In machine learning, the term ''inference'' is sometimes used instead to mean "make a prediction, by evaluating an already trained model"; in this context inferring properties of the model is referred to as ''training'' or ''learning'' (rather than ''inference''), and using a model for prediction is referred to as ''inference'' (instead of ''prediction'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Instrumental Variable
In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term, in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable. Instrumental variable methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur when: # changes in the dependent variable change the value of at least one of the covariates ("reverse" causation), # ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Econometric Models
Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from an economic model which itself is stochastic. However, it is also possible to use econometric models that are not tied to any specific economic theory. A simple example of an econometric model is one that assumes that monthly spending by consumers is linearly dependent on consumers' income in the previous month. Then the model will consist of the equation :C_t = a + bY_ + e_t, where ''C''''t'' is consumer spending in month ''t'', ''Y''''t''-1 is income during the previous month, and ''et'' is an error term measuring the extent to which the model cannot fully explain consumption. Then one objective of the econometrician is to obtain est ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Models
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables. As such, a statistical model is "a formal representation of a theory" ( Herman Adèr quoting Kenneth Bollen). All statistical hypothesis tests and all statistical estimators are derived via statistical models. More generally, statistical models are part of the foundation of statistical inference. Introduction Informally, a statistical model can be thought of as a statistical assumption (or set of statistical assumptions) with a certain property: that the assumption allows us to calculate the probability of any event. As an example, consider a pair of ordinary six ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Causal Inference
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning. Causal inference is widely studied across all sciences. Several innovations in the development and implementation of methodology designed to determine causality have proliferated in recent decades. Causal inference remains especially difficult where experimentation is difficult or impossible, which is common throughout most sciences. The approaches to causal inference are broadly applicable across all types of scientific disciplines, and many methods of causal inference that were designed for certain discip ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
The Annals Of Statistics
The ''Annals of Statistics'' is a peer-reviewed statistics journal published by the Institute of Mathematical Statistics. It was started in 1973 as a continuation in part of the ''Annals of Mathematical Statistics (1930)'', which was split into the ''Annals of Statistics'' and the ''Annals of Probability''. The journal CiteScore is 5.8, and its SCImago Journal Rank is 5.877, both from 2020. Articles older than 3 years are available on JSTOR, and all articles since 2004 are freely available on the arXiv. Editorial board The following persons have been editors of the journal: * Ingram Olkin (1972–1973) * I. Richard Savage (1974–1976) * Rupert Miller (1977–1979) * David V. Hinkley (1980–1982) * Michael D. Perlman (1983–1985) * Willem van Zwet (1986–1988) * Arthur Cohen (1988–1991) * Michael Woodroofe (1992–1994) * Larry Brown and John Rice (1995–1997) * Hans-Rudolf Künsch and James O. Berger (1998–2000) * John Marden and Jon A. Wellner (2001–2003) * Mor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Journal Of Educational Statistics
The ''Journal of Educational and Behavioral Statistics'' is a peer-reviewed academic journal published by SAGE Publications on behalf of the American Educational Research Association and American Statistical Association. It covers statistical methods and applied statistics in the educational and behavioral sciences. The journal was established in 1976 as the ''Journal of Educational Statistics'' and obtained its current name in 1994. The journal's editor is Steven Andrew Culpepper. Mission Statement The ''Journal of Educational and Behavioral Statistics'' (''JEBS'') provides an outlet for papers that are original and useful to those applying statistical approaches to problems and issues in educational or behavioral research. Typical papers will present new methods of analysis. In addition, critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods will be published. Papers discussing statistical ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Guido Imbens
Guido Wilhelmus Imbens (born 3 September 1963) is a Dutch-American economist whose research concerns econometrics and statistics. He holds the Applied Econometrics Professorship in Economics at the Stanford Graduate School of Business at Stanford University, where he has taught since 2012. In 2021, Imbens was awarded half of the Nobel Memorial Prize in Economic Sciences jointly with Joshua Angrist "for their methodological contributions to the analysis of causal relationships." Their work focused on natural experiments, which can offer empirical data in contexts where controlled experimentation may be expensive, time-consuming, or unethical. In 1994 Imbens and Angrist introduced the local average treatment effect (LATE) framework, an influential mathematical methodology for reliably inferring causation from natural experiments that accounted for and defined the limitations of such inferences. Imbens' work with Angrist, together with the work of co-recipient David Card, is cred ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Propensity Score Matching
In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not. Paul R. Rosenbaum and Donald Rubin introduced the technique in 1983. The possibility of bias arises because a difference in the treatment outcome (such as the average treatment effect) between treated and untreated groups may be caused by a factor that predicts treatment rather than the treatment itself. In randomized experiments, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on ave ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal Stratification
Principal stratification is a statistical technique used in causal inference Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference ana ... when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE) in the sense of presenting applications besides all-or-none compliance. The LATE method, which was independently developed by Imbens and Angrist (1994) and Baker and Lindeman (1994) also included the key exclusion restriction and monotonicity assumptions for identifiability. For the history of early developments see Baker, Kramer, Lindeman. Example An example of principal stratification is where there is attrition in a randomized controlled tria ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cambridge University Press
Cambridge University Press is the university press of the University of Cambridge. Granted letters patent by Henry VIII of England, King Henry VIII in 1534, it is the oldest university press in the world. It is also the King's Printer. Cambridge University Press is a department of the University of Cambridge and is both an academic and educational publisher. It became part of Cambridge University Press & Assessment, following a merger with Cambridge Assessment in 2021. With a global sales presence, publishing hubs, and offices in more than 40 Country, countries, it publishes over 50,000 titles by authors from over 100 countries. Its publishing includes more than 380 academic journals, monographs, reference works, school and university textbooks, and English language teaching and learning publications. It also publishes Bibles, runs a bookshop in Cambridge, sells through Amazon, and has a conference venues business in Cambridge at the Pitt Building and the Sir Geoffrey Cass Spo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
