Results 31 to 40 of about 1,239,696 (252)
Polydesigns and Causal Inference
Biometrics, 2005 Summary In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that ...
Li, Fan, Frangakis, Constantine E.
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Recent Developments in Causal Inference and Machine Learning
The Annual review of sociology, 2023 This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal ...
J. Brand, Xiaoping Zhou, Yu Xie
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MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
, 2011 MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods.
Daniel E. Ho +3 more
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Methods and tools for causal discovery and causal inference
WIREs Data Mining Knowl. Discov., 2022 Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy.
Ana Rita Nogueira +4 more
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The mathematics of causal inference [PDF]
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011 I will review concepts, principles, and mathematical tools that were found useful in applications involving causal and counterfactual relationships. This semantical framework, enriched with a few ideas from logic and graph theory, gives rise to a complete, coherent, and friendly calculus of causation that unifies the graphical and counterfactual ...
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Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Human Molecular Genetics, 2014 Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes.
G. Davey Smith, G. Hemani
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Proceedings of the AAAI Conference on Artificial Intelligence, 2017 We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is simple in the true ...
Murat Kocaoglu +3 more
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CoRR, 2021 We investigate causal inference in the asymptotic regime as the number of variables approaches infinity using an information-theoretic framework. We define structural entropy of a causal model in terms of its description complexity measured by the logarithmic growth rate, measured in bits, of all directed acyclic graphs (DAGs), parameterized by the ...
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Placebo Tests for Causal Inference
American Journal of Political Science, 2023 Placebo tests are increasingly common in applied social science research, but the methodological literature has not previously offered a comprehensive account of what we learn from them.
Andrew C. Eggers +2 more
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An Introduction to Causal Inference [PDF]
The International Journal of Biostatistics, 2010 This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the ...
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