Causal model

About: Causal model is a research topic. Over the lifetime, 5330 publications have been published within this topic receiving 255990 citations.

Causality: models, reasoning, and inference

Abstract: 1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.

Use of partial least squares (PLS) in strategic management research: a review of four recent studies

Abstract: Advances in causal modeling techniques have made it possible for researchers to simultaneously examine theory and measures. However, researchers must use these new techniques appropriately. In addition to dealing with the methodological concerns associated with more traditional methods of analysis, researchers using causal modeling approaches must understand their underlying assumptions and limitations. Most researchers are well equipped with a basic understanding of LISREL-type models. In contrast, current familiarity with PLS in the strategic management area is low. The current paper reviews four recent studies in the strategic management area which use PLS. The review notes that the technique has been applied inconsistently, and at times inappropriately, and suggests standards for evaluating future PLS applications. Copyright © 1999 John Wiley & Sons, Ltd.

Causation, prediction, and search

Abstract: What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

Statistics and Causal Inference

Abstract: Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling.

Marginal Structural Models and Causal Inference in Epidemiology

Abstract: In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.