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FAST RESTRICTED CAUSAL INFERENCE [PDF]
Demonstratio Mathematica, 2000 Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm "accelerating" the known CI algorithm of Spirtes, Glymour and Scheines {Spirtes:93}.
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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, G. Tuñón, A. Dafoe
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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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Causal Inference in the Social Sciences
Annual Review of Statistics and Its Application, 2023 Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data.
G. Imbens
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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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Revista de la Facultad de Ciencias de la Salud, 2014 Establishing causality has been a problem throughout history of philosophy of science. This paper discusses the philosophy of causal inference along the different school of thoughts and methods: Rationalism, Empiricism, Inductive method, Hypothetical ...
Richard Shoemaker
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International Journal of Infectious DiseasesObjectives: To quantify the incidence of adverse events after COVID-19 vaccination and COVID-19 diagnosis in women of reproductive age; to examine pregnancy as a potential risk modifier.
Stacey L. Rowe +6 more
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环境与职业医学In environmental epidemiological research, extensive non-random environmental exposures and complex confounding biases pose significant challenges when attempting causal inference.
Hui SHI +6 more
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